Identification of noncoding regulatory variants impacting the pharmacogenomics of ALL treatment

Single-nucleotide variants (SNVs) impacting diverse pharmacological traits in ALL were identified for functional interrogation. We chose SNVs associated with relapse or persistence of MRD after induction chemotherapy in childhood ALL patients to investigate the role of inherited noncoding regulatory variants impacting clinical phenotypes (i.e., treatment outcome). These SNVs were identified from published GWAS of ALL patients enrolled in St. Jude Childrens Research Hospital and the Childrens Oncology Group clinical protocols3,4,5 (see Methods for variant selection criteria). Variant selection also included prioritization for treatment outcome SNVs associated with drug resistance phenotypes in primary ALL cells to enrich for variation impacting ALL cell biology (see Methods for variant selection criteria). These treatment outcome-associated variants, as well as all variants in high LD (r2>0.8) with the sentinel GWAS variants, were further evaluated (Fig.1a, b).

a SNVs of interest from GWAS were pursued based on association with ex vivo chemotherapeutic drug resistance in primary ALL cells from patients and/or treatment outcome. Dex dexamethasone, Pred prednisolone, VCR vincristine, 6MP 6-mercaptopurine, 6TG 6-thioguanine, LASP L-asparaginase. b GWAS SNVs were combined with ALL disease susceptibly control GWAS SNVs and SNVs in high LD (R2>0.8) and c mapped to accessible chromatin sites in ALL cell lines, ALL PDXs, and primary ALL cells from patients. Of the 1696 SNVs mapped to accessible chromatin sites, 35 are control SNVs. Source data are provided in the Source Data file.

We also identified variants directly associated with ex vivo chemotherapeutic drug resistance in primary ALL cells from patients by performing GWAS analyses using SNV genotype information and ex vivo drug resistance assay results for six antileukemic agents (prednisolone, dexamethasone, vincristine, L-asparaginase, 6-mercaptopurine [6MP] and 6-thioguanine [6TG]) in primary ALL cells from 312344 patients (not all patients were tested for all drugs) enrolled in the Total Therapy XVI clinical protocol at St. Jude Childrens Research Hospital (see Methods). We further prioritized functional ex vivo drug resistance SNVs by determining if they were eQTLs in primary ALL cells or related cell types (i.e., whole blood and EBV-transformed lymphocytes) from the Genotype-Tissue Expression (GTEx) consortium37 (see Methods for variant selection criteria). Ex vivo drug resistance variants that were also identified as eQTLs, as well as variants in high LD (r2>0.8) with these sentinel GWAS variants, were further evaluated (Fig.1a, b).

GWAS have also been performed for childhood ALL disease susceptibility and identified several GWAS loci harboring variants with genome-wide significance44,45,46,47,48,49,50. Several follow-up studies of these GWAS loci have identified candidate causal noncoding variants and mechanisms involving gene regulatory disruptions51,52,53. As a result, we used ALL disease susceptibility variants (n=11), as well as variants in high LD (r2>0.8) with them, for further analysis as positive controls in our study (Fig.1a, b).

Because most of these variants map to noncoding portions of the human genome, these data point to disruptions in gene regulation as the underlying mechanism of how these variants impact ALL cell biology. We therefore utilized assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq)54 chromatin accessibility data in 161 ALL cell models, comprised of primary ALL cells (cryopreserved, n=2455; fresh, n=12056), ALL cell lines (n=14) and ALL patient-derived xenografts (PDXs, n=3), to uncover which variants map to putative CREs in ALL cells57 (i.e., regulatory variants; Fig.1c). Although we detected variation in ATAC-seq TSS enrichment scores and peak counts that is to be expected from such a large, mixed cohort of ALL cell models, the peaks called were largely reproducible (found in >3 samples) within each group (Supplementary Fig.1ac). ATAC-seq data from primary ALL cells, ALL cell lines, and PDXs were combined and identified 1696 regulatory variants at accessible chromatin sites in ALL cells for functional investigation (Fig.1c and Supplementary Data1).

To examine the functional effects of these 1696 regulatory variants on transcriptional output in a high-throughput manner we utilized a barcode-based MPRA platform29,32 to measure differences in allele-specific transcriptional output (Fig.2a). Oligonucleotides containing 175-bp of genomic sequence centered on each reference (ref) or alternative (alt) variant allele, a restriction site, and a unique 10-bp barcode sequence were cloned into plasmids. An open reading frame containing a minimal promoter driving GFP was then inserted at the restriction site between the alleles of interest and their unique barcodes (Fig.2a). We utilized 28 unique 3UTR DNA barcodes per variant allele (56 barcodes per regulatory variant), and variants near bidirectional promoters (47 total variants) were tested using both sequence orientations. In total, 97,608 variant-harboring oligonucleotides were evaluated for allele-specific differences in gene regulatory activity (Fig.2a).

a Diagram describing design of MPRA (also see Methods). bd Significant MPRA hits were identified by BenjaminiHochberg FDR corrected two-tailed Students T tests. b Distribution of significant changes in allele-specific transcriptional activity across all SNVs. c Number of MPRA SNVs showing significant (Adj. p<0.05) changes in allele-specific transcriptional activity in each ALL cell line. d Pairwise linear correlation between changes in allele-specific transcriptional activity for all significant (Adj. p<0.05) changes across all cell lines. R2 correlation and p value are provided. All source data and statistical parameters are provided in the Source Data file.

Following transfection into 7 different B-cell precursor ALL (B-ALL; 697, BALL1, Nalm6, REH, RS411, SEM, SUPB15) and 3 T-cell ALL (T-ALL; CEM, Jurkat, P12-Ichikawa) human cell lines (n=4 transfections per cell line; 40 total), the transcriptional activity of each allele variant was measured by high-throughput sequencing to determine the barcode representation in reporter mRNA and compared to DNA counts obtained from high-throughput sequencing of the MPRA plasmid pool (Fig.2a). In the 10 cell lines MPRA detected 4633 instances of significant differential activity between alleles across 91% (1538/1696) of regulatory variants tested (Fig.2b, c, Supplementary Data2). The 10 ALL cell lines showed substantial differences in the total number of regulatory variants harboring significant allele-specific activity, which we suspect largely stems from differences in transfection efficiency (Fig.2c). Importantly, when comparing changes in allele-specific MPRA activity for each regulatory variant we found that significant changes in activity (adj. p<0.05) were highly correlated between ALL cell lines, with 87% concordance in allelic-specific activity, suggesting that significant MPRA hits were likely to be robust and reproducible between cell lines (Fig.2d). Allele-specific MPRA activities were also correlated using all pairwise cell line comparisons for each regulatory variant, irrespective of significance (Supplementary Fig.2a). Importantly, 31 of the 35 positive control variants (i.e., ALL disease susceptibility-associated variants and variants in high LD) showed significant allelic effects in at least 1 cell line, and 10 showed significant and concordant allelic effects in at least three ALL cell lines, including two variants (rs3824662 at GATA3 locus and rs75777619 at 8q24.21) directly associated with ALL susceptibility44,49,52 (Supplementary Data2). The risk A allele at rs3824662 was associated with higher GATA3 expression and chromatin accessibility and demonstrated significantly higher allele-specific activity in our MPRA44,52, thereby demonstrating that the MPRA could detect allelic effects previously identified by others.

To further validate MPRA hits in an ex vivo model, we performed MPRA using two B-ALL PDX samples that were freshly harvested from mice. These samples detected 26 and 67 significant gene regulatory variants, respectively, and showed significant correlation with the cell line MPRA data (Supplementary Fig.2b, c, Supplementary Data3). We attribute the detection of relatively lower numbers of variants in PDXs to technical effects stemming from poor transfection efficiency and limited cell survival ex vivo. Overall, our data suggest that the cohort of SNVs tested contained functional regulatory variants with the potential to impact gene regulation.

To further focus on regulatory variants most likely to broadly impact gene regulation in ALL cells, we prioritized 556 variants with significant (adj. p<0.05) and concordant allele-specific activities in at least three ALL cell lines (i.e., functional regulatory variants; Fig.3ad, Supplementary Data4). Most of these functional regulatory variants (318/556) mapped to accessible chromatin found only in primary ALL cell samples, underscoring the importance of incorporating chromatin architecture from primary ALL cells, and 54 functional regulatory variants mapped to transcription factor footprints in primary ALL cells (Supplementary Fig.3). Additionally, we used Genomic Regions Enrichment of Annotations Tool (GREAT) to associate these SNVs with their nearby genes and search for enrichment in gene ontology biological processes pathways58. Although GREAT identified gene associations for nearly all SNVs, we found no significant pathway associations (Supplementary Data4 and 5). Because further functional investigation of variants in primary ALL cells or PDXs ex vivo is largely intractable, we focused on 210 functional regulatory variants that were detected in open chromatin in one of the 14 ALL cell lines that we had generated ATAC-seq data (Fig.3d). Most of these variants (159/210; 76%) were also found in accessible chromatin in PDXs and/or in primary ALL cells from patients (Fig.3d).

a 556 of the 1696 SNVs assayed are functional regulatory variants with reproducible (FDR<0.05 in >2 cell lines) and concordant (same directionality in >2 cell lines) changes in allele-specific activity. b Frequency distribution plot showing the number of cell samples showing concordant and significant MPRA activity of variants. c Plot showing the distribution of log2-adjusted activity between alternative (Alt) and reference (Ref) alleles across 556 functional regulatory variants. 210 SNVs (in blue) mapped to accessible chromatin sites in ALL cell lines and 346 SNVs (in black) mapped only to accessible chromatin sites identified in primary ALL cells and/or PDXs. d Upset plot shows how many functional regulatory variants map to open chromatin in diverse ALL cell models. 210 of the 556 functional regulatory variants are found in accessible chromatin sites that were identified in an ALL cell line. Source data are provided in the Source Data file.

For additional validation using traditional luciferase reporter assays, we prioritized these 210 functional regulatory variants based on allele-specific effect size and selected high-ranking SNVs. Dual-luciferase reporter assays showed similar allele-specific changes in activity to that which was detected by MPRA for 7 SNVs tested (Supplementary Fig.4ak). In fact, a significant positive correlation (p=0.0017) was observed between the allelic effects detected by MPRA and luciferase reporter assays (Supplementary Fig.4l). Together, these analyses assessed the robustness of our MPRA screen of functional regulatory variants and identified 556 SNVs with reproducible and concordant allele-specific effects on gene regulation. Importantly, 210 of the 556 significant hits that were concordant in at least three cell lines were found in open chromatin sites in ALL cell lines and, therefore, warranted further exploration.

To better understand how these variants impact cellular phenotypes, we first determined if the 210 functional regulatory variants found in accessible chromatin sites in ALL cell lines could be directly associated with a target gene. While 35 functional regulatory variants were localized close (2.5kb) to nearby promoters (Fig.4a, Supplementary Data4 and 6), 175 variants were promoter-distal (>2.5kb), and therefore likely to map to CREs with unclear gene targets (Fig.4a). While CREs are often associated with the nearest genes, 3D chromatin looping methods are a more reliable method to associate a CRE with its target gene promoter. In pursuit of evidence-based association of promoters and specific CREs, we performed two related chromatin looping methods, H3K27Ac HiChIP59 and promoter capture HiC (CHiC)39, in 8 of 10 ALL cell lines used in MPRA and determined that 19 of the 175 non-promoter functional regulatory variants showed connectivity to distal promoters in the same cell line where allele-specific MPRA activity and chromatin accessibility were detected (Fig.4a, Supplementary Data6). Interestingly, H3K27Ac HiChIP and promoter CHiC called similar numbers of loops across all 8 cell lines (690,579 versus 660,313, respectively), but promoter CHiC loop calling was more consistent per cell line (Supplementary Fig.5, Supplementary Data7). HiChIP detected no looping at any of the 556 reproducible and concordant SNVs from the MPRA, and the 19 SNVs showing connectivity to a promoter were solely detected by promoter CHiC, further highlighting the utility of this method in GWAS-oriented studies41,60,61,62,63.

a Data show the number of functional regulatory variants mapping to open chromatin in cell lines that associate directly with promoters (within 2.5kb) or that are distally promoter-connected via promoter CHiC. b MPRA data show distal regulatory variants in accessible chromatin (some promoter-connected by promoter CHiC data) exhibit stronger effects on allele-specific activity than promoter-associated functional regulatory variants. ANOVA with KruskalWallis test was performed with Dunns correction for multiple comparisons. c Amongst distally promoter-connected functional regulatory, variants that map to intronic and distal intergenic sequences showed greater activity than those in UTRs. ANOVA with KruskalWallis test was performed with Dunns correction for multiple comparisons. d, e Data show the ranked allele-specific activity distribution of MPRA data for d promoter-associated functional regulatory variants and e distally promoter-connected functional regulatory variants. All source data and statistical parameters are provided in the Source Data file.

In prioritizing functional regulatory variants, we were interested in the gene regulatory impact of variants at TSS-proximal promoter-associated versus TSS-distal promoter-connected CREs as measured by MPRA. Interestingly, we found that SNVs found at TSS-distal open chromatin sites, promoter-associated or not, showed higher allele-specific changes in MPRA activity than those at promoters (Fig.4b). While we acknowledge that many of the 156 variants for which we did not detect a relationship with a promoter are likely to have meaningful gene targets, we focused on CREs containing variants with known gene targets in ALL cells for functional validation. Amongst the TSS-distal promoter-connected functional regulatory variants, we found that distal intergenic and intronic SNVs showed significantly higher allele-specific activity than those in UTRs (Fig.4c). These data suggest that the most robust allelic effects attributable to these regulatory variants are likely to occur at distal intergenic and intronic sites >2.5kb from the TSS of the target gene.

Next, we ranked TSS-proximal promoter-associated and TSS-distal promoter-connected functional regulatory variants by the geometric mean of their significant MPRA data to account for the magnitude of allele-specific activity and the reproducibility of a significant change across ALL cell lines (Fig.4d, e). This analysis identified rs1247117 as the most robust functional regulatory variants, which we then pursued for mechanistic understanding (Fig.4e).

We pursued functional validation of rs1247117 based on its highest-ranking geometric mean of MPRA allelic effect. rs1247117 is in high LD with two GWAS sentinel variants (rs1312895, r2=0.99; rs1247118, r2=1) that are associated with persistence of MRD after induction chemotherapy3. This functional regulatory variant maps to a distal intergenic region harboring chromatin accessibility downstream of the CACUL1 gene, for which it is an eQTL in EBV-transformed lymphocytes37. However, we found that rs1247117 loops to the EIF3A promoter in Nalm6 B-ALL cells (Fig.5a). We, therefore, explored how this accessible chromatin site might recruit transcriptional regulators that would depend on the allele present at rs1247117. For this, we first performed ChIP-seq for RNA pol II and H3K27Ac, which confirmed RNA Pol II occupancy and H3K27Ac enrichment in Nalm6 cells, indicating that rs1247117 is associated with an active CRE (Fig.5a). Through an examination of the underlying DNA sequence spanning rs1247117, we found that the reference guanine (G) risk allele at rs1247117 resides in a PU.1 transcription factor binding motif that is disrupted by the alternative adenine (A) allele (Fig.5b). Although the risk G allele is the reference allele, the alternative A allele is more common in human populations. Supporting PU.1 binding at this location, accessible chromatin profiling in primary ALL cells identified an accessible chromatin site and PU.1 footprint spanning rs1247117 in diverse ALL samples (Supplementary Fig.6a, b). Significantly greater chromatin accessibility at rs1247117 was also observed in heterozygous (GA) patient samples compared to patient samples homozygous for the alternative A allele (Supplementary Fig.6c), and the G allele at rs1247117 harbored significantly greater ATAC-seq read count compared to the A allele (Supplementary Fig.6d). Importantly, we determined that PU.1 was bound at this site in Nalm6 cells using CUT and RUN64 (Fig.5a).

a IGV genome browser image in Nalm6 cells showing the genomic context, chromatin accessibility, and EIF3A promoter connectivity using promoter CHiC of the top functional regulatory variant, rs1247117, with the highest allele-specific MPRA activity. Genomic binding profiles are also shown for RNA polymerase II (RNA Pol2), histone H3 lysine 27 acetylation (H3K27Ac), and PU.1. b rs1247117 lies in a PU.1 binding motif. The human genome reference sequence, Nalm6 genome sequence, location of rs1247117, and PU.1 position weight matrix are shown. c Design of biotinylated DNA probes for in vitro rs1247117 pulldown. d Biotinylated DNA pulldown shows rs1247117 allele-dependent enrichment of PU.1 binding. Blot shown is representative of two independent experiments. Densitometric quantification of two blots is shown. e CRISPR/Cas9 was used to change the allele at rs1247117 from A>G in Nalm6 cells. Data show the location of gRNA and ssODN, as well as NGS reads obtained from clone 1 and 2 at rs1247117. f PU.1 ChIP-PCR shows increased PU.1 binding at the rs1247117 locus using two A>G modified clones and 3 primer sets. Data shown are meanSD of three independent experiments for each primer set. Two-way ANOVA with Dunnetts multiple comparisons correction, n=3. g ATAC-seq data normalized for frequency of reads in peaks (FRIP) show a significantly higher count of G nucleotides in two clones of A>G modified Nalm6 cells compared to the count of A nucleotides detected in parental Nalm6 cells. Data shown are the meanSD. Bonferroni corrected, two-tailed Students T tests, n=3. h Western blots and quantification showing decreased EIF3A expression in A>G modified Nalm6 cells. Blots shown are representative of three independent experiments. Quantification data shown are the meanSD. Two-tailed Students T tests compare parental Nalm6 to combined data from A>G clones, n=3. All source data and statistical parameters are provided in the Source Data file.

Nalm6 cells contain the alternative A allele that disrupts the PU.1 motif at rs1247117, yet our data suggests that this site still binds PU.1 (Fig.5a, b). This led us to hypothesize that PU.1 binding affinity for the PU.1 motif surrounding rs1247117 would be strengthened by the risk G allele. Therefore, we designed biotinylated DNA probes containing two tandem 25-bp regions centered on reference G or alternative A allele-containing rs1247117 to test this hypothesis (Fig.5c). Using biotinylated probes, we performed an in vitro DNA-affinity pulldown from Nalm6 nuclear lysate and found that while PU.1 was indeed bound to the alternative A allele, PU.1 was more robustly bound to the reference G allele at rs1247117 (Fig.5d). To further assess the impact of the rs1247117 allele on PU.1 binding, we changed the Nalm6 allele from A to G using CRISPR/Cas9 (Fig.5e; AA = parental genotype, GG = mutated genotype). We used ChIP-PCR to determine that PU.1 binding was increased with the G allele relative to the A allele at the CRE containing rs1247117 in two A>G Nalm6 clones across 3 unique primer sets within the PU.1 peak at rs1247117 that was detected in Nalm6 cells (Fig.5f). We then asked if transposase accessibility was also increased at the CRE containing rs1247117 when the G allele was present. Using ATAC-seq, we found that accessibility was indeed increased at rs1247117 in mutated Nalm6 cells with the G allele when compared to the parental Nalm6 cells containing the A allele (Fig.5g). These data suggest that the risk G allele increases genomic accessibility and the affinity of PU.1 binding at rs1247117 relative to the alternative A allele.

We were next interested in how allele-specific PU.1 binding at rs1247117 was related to the expression of the protein encoded by the connected gene, EIF3A. We found that the G allele, which increased recruitment of PU.1, resulted in decreased expression of EIF3A when compared to Nalm6 cells with the A allele (Fig.5h). These data suggest that PU.1 recruitment to the CRE containing rs1247117 results in a net-repressive effect on EIF3A protein levels, and that less PU.1 recruitment with the A allele results in greater EIF3A expression.

Clonal selection can lead to the accumulation of random SNVs and even larger structural variations65 that can confound functional interpretation of more complex trans phenotypic effects. Therefore, to examine the connection between rs1247117 and the persistence of MRD after induction chemotherapy, we decided to use CRISPR/Cas9 to delete the CRE containing rs1247117 in heterogeneous cell pools of Nalm6 and SUPB15 cells (rs1247117 del) to avoid clonal selection (Fig.6a, b, Supplementary Fig.7a). Given that loss of the CRE containing rs1247117 would abolish PU.1 recruitment at this region, we hypothesized that rs1247117 del would result in increased EIF3A expression. Accordingly, we found that EIF3A expression was elevated in rs1247117 del cells relative to parental Nalm6 and SUPB15 cells, respectively (Fig.6c, d, Supplementary Fig.7b), further supporting an inverse relationship between PU.1 binding at rs1247117 and EIF3A expression.

a Diagram on the left showing the genomic context of the rs1247117 CRE deletion in Nalm6 cells in relation to chromatin accessibility, PU.1 binding and rs1247117. Black bar represents ATAC-seq peak, green par represents PU.1 peak, and red bar represents region deleted using CRISPR/Cas9 genome editing. b Gel shows validation of deletion using primers flanking deleted region. Arrow points to PCR fragment with deletion in heterogeneous Nalm6 cell pools harboring deletion compared to wild-type parental Nalm6 cells. c EIF3A gene expression is upregulated upon deletion of the CRE containing rs1247117. RT-qPCR data show the meanSD of three independent experiments. Two-tailed Students T test. d Western blots and quantification showing increased EIF3A expression in rs1247117 del Nalm6 cells. Blots shown are representative of four independent experiments. Quantification data show the meanSD. Two-tailed Students T tests, n=4. eg Drug sensitivity data comparing viability relative to vehicle treatment of wild-type parental Nalm6 cells and Nalm6 cells with rs1247117 CRE deletion after vincristine (VCR) treatment for 24 (n=3), 48 (n=3) and 72 (n=3) hours at the indicated concentrations. Non-linear regression and F test analysis indicate that these dose-response curves are significantly different. h Caspase 3/7 activity assays comparing Caspase activity relative to vehicle treatment of wild-type parental Nalm6 cells and Nalm6 cells with rs1247117 CRE deletion after vincristine (VCR) treatment for 72hours at the indicated concentrations (n=3). Dose-response curves of non-linear regression indicate that these curves are significantly different. Non-linear regression and F test analysis indicate that these dose-response curves are significantly different. All source data are provided in the Source Data file.

Because the risk G allele at rs1247117 was also associated with vincristine resistance in primary ALL cells from patients, we additionally sought to determine the impact of the CRE deletion containing rs1247117 on cellular response to vincristine treatment. We hypothesized that because the risk G allele is associated with enhanced PU.1 binding and resistance to vincristine, complete disruption of PU.1 binding in Nalm6 cells harboring the CRE deletion would show increased sensitivity to vincristine relative to parental Nalm6 cells. As predicted, Nalm6 cells with the CRE deletion exhibited significantly increased sensitivity to vincristine across a range of concentrations after 24, 48, and 72hours of treatment (Fig.6eg), and we found consistent effects on cell viability in SUPB15 cells (Supplementary Fig.7c). Consistent with enhanced sensitivity to vincristine, we also found increased caspase 3/7 activity in rs1247117 del Nalm6 cells relative to parental Nalm6 cells after 72hrs and across a range of vincristine concentrations (Fig.6h). These data suggest that a functional regulatory variant alters the binding affinity of a key transcription factor, PU.1, and disruption of this locus impacts EIF3A expression and vincristine sensitivity in ALL cells. To further validate our methodology utilizing CRISPR/Cas9 to delete CREs, we deleted CREs spanning two additional top variants, rs7426865 and rs12660691 (see Fig.4e), that was associated with the ex vivo resistance to 6-mercaptopurine and dexamethasone, respectively, in primary ALL cells. Deletion of these CREs also impacted protein expression and sensitivity to the associated chemotherapeutic agent, thereby supporting our functional approach (Supplementary Figs.8 and 9).

We next wanted to connect EIF3A directly to vincristine resistance. Given that EIF3A is an essential gene per the Broad Institutes DepMap, we opted to test the hypothesis EIF3A overexpression alone was sufficient to impact the Nalm6 cell response to vincristine. We, therefore, used lentiviral transduction to overexpress EIF3A in Nalm6 cells and compared EIF3A overexpression (EIF3A OE) cells to control infected cells (Nalm6 WT, Supplementary Fig.10a). Using two independent infections of EIF3A OE, we found that at 48hr and 72hr, EIF3A OE cells were more sensitive to vincristine than Nalm6 WT cells (Supplementary Fig.10b). These data suggest that EIF3A expression impacts the ALL cell response to vincristine, with higher expression sensitizing cells to the drug, and further establishes this gene as the likely target of the association.

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Pharmacogenomic studyA pilot study of the effect of pharmacogenomic phenotypes on the adequate dosing of ... - Nature.com

At present, pharmacogenomic tests are not available for all medications and are not widely employed as preventive measures in patient care. Globally, health insurance often does not even cover pharmacogenomic tests. This may change in the future, however, especially as pharmacogenomic testing becomes less expensive. Since an individual's genetic makeup remains constant, a pharmacogenomic test only needs to be performed once and bring lifelong benefits.

Challenges in broader adoption

There are several challenges to turning pharmacogenomictesting into routine practice:It would require investments in both technology and upskilling the workforce. Healthcare systems across the globe face the challenge of moving care upstream and moving to more preventative models of care,according to Videha Sharma, clinical innovation lead for the University of Manchester. "The prescribing of medicines is the most common therapeutic intervention in healthcare and offers a fantastic opportunity to avoid harmful side effects to make medicines more effective from the start. As such, there is a huge potential to boost the way we manage diseases at scale," Sharma said.

Current clinical use cases

Pharmacogenomics is gradually being introduced into clinical care, though it has not yet become a standard practice. In 2023, the National Institute for Health and Care Excellence (NICE) published draft guidance recommending point-of-care genomic testing for people who have had a stroke. The purpose of this test is to detect whether there have been changes in a gene called CYP2C19. This specific mutation can guide prescribing.

For example, in cardiology, patients with coronary artery disease, vascular diseaseor stroke are often prescribed a drug called clopidogrel. However, a patient may be a poor metabolizerof the drug, which CYP2C19 testing would revealin such cases, the patient would be offered an alternative.

Another example of whenpharmacogenomic testing is valuable is prior to administering the antibiotic gentamicin to infants, since one in 500 babies can suffer permanent hearing loss when prescribed this drug.This can be prevented by detecting the CYP2C19 mutation.

However, there are uncertainties around how to implement testing, how to share results across care settings and what the role of patients is so theyfeel empowered to receive personalised medicines. As a result, Sharma advocates for strong multi-disciplinary and cross-industry collaboration and has actively helped build a team of clinicians, designers, technologists and public contributors.

Upcoming plans of the NHS

In 2022, the British Pharmacological Society and the Royal College of Physicians published a report that calls for pharmacogenomic testing to be integrated fully, fairly and swiftly into the NHS in the UK. According to the authors, this will empower healthcare professionals to deliver better, more personalised care, and in turn improve outcomes for patients and reduce costs to the NHS.

The desire to advance pharmacogenomics in the clinical practice is there; it will simply require some time to achieve this goal. The "what" and the "why" have been clearly stated and are obvious to most key stakeholders the question of "how"still remains, and bridging the gap, genomics and digital health together will help realise the benefits of pharmacogenomics to patients and populations.

Clinical Innovation Lead for the University of Manchester Videha Sharma will be speaking at the Precision Digital Solutions for Personalised Care session during the 2024 HIMSS European Health Conference & Exhibition, which is scheduled to take place 29-31 May2024in Rome. Learn more and register.

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Pharmacogenomics could improve medication safety and reduce waste - Healthcare IT News

WEDNESDAY, Nov. 22, 2023 (HealthDay News) -- For patients with major depressive disorder, pharmacogenomics testing to guide antidepressant use yields population health gains and reduces health system costs, according to a study published online Nov. 14 in CMAJ, the journal of the Canadian Medical Association.

Shahzad Ghanbarian, Ph.D., from the University of British Columbia in Vancouver, Canada, and colleagues developed a discrete-time microsimulation model of care pathway for major depressive disorder in British Columbia to examine the effectiveness and cost-effectiveness of pharmacogenomic testing from the public payer's perspective. Incremental costs, life-years, and quality-adjusted life-years (QALYs) were estimated for a representative cohort of patients.

The researchers found that pharmacogenomic testing was predicted to save the British Columbia health system $956 million over 20 years ($4,926 per patient) and bring health gains of 0.064 and 0.381 life-years and QALYs per patient, respectively, if implemented for adult patients with moderate-to-severe major depressive disorder. The savings were mostly as a result of slowing or preventing the transition to refractory depression. Over 20 years, pharmacogenomic-guided care was associated with 37 percent fewer patients with refractory depression. The costs of pharmacogenomics testing would be offset within about two years of implementation as estimated in sensitivity analyses.

"Interventions that might improve remission rates and reduce the number of cases of refractory depression, in particular, are needed to improve the quality of life for patients, and reduce the economic burden of major depressive disorder on already strained health care systems," the authors write.

Abstract/Full Text

Editorial

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Pharmacogenomic Testing in Major Depression: Benefits, Cost ... - HealthDay

American supplier Thermo Fisher Scientific has launched the new Axiom PangenomiX Array, its largest and most ethnically diverse array to date, offering optimal genetic coverage for population scale disease studies and pharmacogenomic research.

The PangenomiX Array is currently the only research solution that combines four assays in one test: SNP genotyping, whole genome copy number variant detection, fixed copy number discovery, blood and HLA typing. The high-throughput array is designed to advance disease risk and detection research, population-scale disease research programs, ancestry and wellness testing, drug efficacy testing, and drug development research.

Inclusive of clinically relevant pharmacogenomic markers and pathogenic variants, the PangenomiX Array offers researchers enhanced whole-genome imputation and a high level of diversity for testing different ethnicities to keep pace with the growing understanding of the genome. The array has already been used to analyse nearly half a million ethnically diverse samples at a predominant biobank in the US to advance more inclusive research studies related to the prevention, diagnosis and treatment of disease.

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Thermo Fisher unveils its largest and most ethnically diverse array for pharmacogenomic research - BSA bureau

The Cincinnati VA Medical Center (CVAMC) now offers Pharmacogenomic (PGx) testing for Veterans enrolled in VA health care.

The pharmacogenomics testing for veterans (PHASER) program gives you the opportunity to work with VA providers on determining which medications are most effective for you based on your genetics, and it only requires one blood test.

What is the PHASER program and how does it benefit me?

The PHASER program supports free PGx testing for Veterans. PGx testing under the PHASER program can help reduce medication trial and error and hospitalizations caused by adverse reactions to medicines. The PHASER program can be utilized within multiple areas of care, including mental health, pain management, infectious disease, oncology, cardiology, gastroenterology, transplant specialty (immunology), and more with continuous expansion into other specialties and medications.

What is Pharmacogenomics (PGx) and how do I pronounce it?

Pharmacogenomics (PGx), pronounced far-ma-CO-gen-o-MIX, uses information from genes to assist in understanding how a person responds to medicines. Genes are part of DNA that provides instructions on how the body develops and functions. Because people have differences in their genes, they may respond to medicines differently.

What is PGx testing?

PGx testing is a type of genetic testing that focuses on how your body processes or responds to medicines. Along with other medical information, PGx test results help providers determine if there is a better type of medication or dose for an individual.

Will PGx testing inform me of my risk for any diseases?

The PGx test may identify an increased risk for certain, uncommon, health conditions that were passed down to you from your parents. In this case, you and your provider will be informed, and your provider will talk to you about what (if any) next steps are recommended.

What are the limitations of PGx testing?

Genetics illuminates only part of a persons story. Other considerations like age, overall health, other medicines, and body size also play a role in how you respond to medication.

Is there harm associated with PGx testing?

A blood draw is all you need to have PGx testing done. Like other tests that require blood draws, PGx testings risk is low.

Is PGx testing available to me?[BSJV1]

Currently, Veterans who receive health care at Cincinnati VAMC and other participating VA facilities, can get PGx testing done. Talk to your VA provider(s) about PGx testing. They will explain the test, answer any questions you or your caregiver have, and if you are interested in moving forward, your provider will order the test for you.

How do I get the test done?

PGx testing requires a blood draw, which you can get done at the VA.

How long does it take to receive my results?

It may take up to 2 weeks for results to be available for you and your provider. You will receive an easy to read report that you can discuss with your provider(s) to see if changes are necessary. Do not change any medications prior to talking to your provider(s).

Next steps

Veterans interested in the PHASER program should contact their primary care provider to discuss PGx testing.

For more information about the PHASER program and PGx testing, click here.

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VA Pharmacogenomics Program Offers Opportunity For Safer, More Effective Medication Therapy - Veterans Affairs

Genomics and Precision Medicine Expo taking place on May 23 and 24 2023 at ExCeL London has announced its top 5 highlights for this years inaugural conference.

The event, which is being run in partnership with Genomics England, will explore the latest advancements and developments in genome sequencing and genomic testing, and the immediate and future potential for the development of precision medicine within the NHS and beyond.

Content throughout the two days will cover topics including discovery, research, development, and regulatory assessment, through to delivery, supply and patient referral.

Lucy Clarke, event manager of Genomics and Precision Medicine Expo, said: The event is a unique chance to discover critical updates, gain practical learnings that you can apply to your own work, and share insights with like-minded peers across patient care and science. With the UK being a key world leader in genomics testing and life sciences, there is a wealth of knowledge to be shared at the Genomics and Precision Medicine Expo.

The first day includes a session on precision cancer medicine: progress, limitations and opportunities. Precision medicine is the desire to tailor each persons treatment according to the underlying biology of their disease.

Also on the first day, a talk in the afternoon will examine implementing whole genome sequencing into routine clinical practice. All children in the U.K. with cancer are eligible for somatic and germline whole genome sequencing (WGS) via the NHS Genomic Medicine Service. Jack Bartram, a consultant pediatric hematologist at Great Ormond Street Hospital for Children (GOSH), will describe experiences of using WGS for hematological malignancies to obtain, analyze and clinically integrate results in a meaningful timeframe.

Another highlight is a panel discussion on navigating patient consent in cancer genomics. The use of genomic testing in routine care brings benefits for patient care and treatment, but it can also present new challenges for clinicians around consent. In this panel session involving representatives from Genomics England and The Royal Marsden NHS Foundation Trust, guests will explore different scenarios and approaches to consenting cancer patients for genomic testing.

On the second day, an afternoon presentation will look at the role of nurses in transforming genomic healthcare. The application of genomics in everyday practice is now a reality in cancer care and in rare diseases. Tootie Bueser, director for nursing & midwifery and chief nurse Southeast Genomic Medicine Service Alliance and North Thames Genomic Medicine Service Alliance will explore how nurses have an important role in transforming care through genomics and need access to education, training and other resources to maximize this opportunity.

The final highlight is a session on pharmacogenomics, hosted by Nisha Shaunak, associate chief pharmacist Cancer, TRU and Surgery Clinical Group, Guys & St Thomas NHS Foundation Trust.

Co-located with the Oncology Professional Care, Genomics and Precision Medicine Expo, the event is free to attend for registered healthcare professionals and individuals working in the field of genomics and precision medicine (excluding commercial companies) and is fully CPD accredited.

The co-located events feature 150+ sessions across seven focused theaters with product and service providers showcasing the latest innovations on the exhibition floor.

Genomics and Precision Medicine Expo intends to capture the interest of Public Health and ICS leaders, CCIOs within the NHS and private sector, clinicians working in acute and primary care who wish to develop their knowledge and understanding of the fast-moving developments in this sector, and clinical and biomedical scientists.Attendance is free for UK healthcare professionals and individuals working in the fields of genomics and precision medicine (in non-commercial roles). Register here: genomicsprecisionmedicine.co.uk

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5 highlights at first Genomics and Precision Medicine Expo - Labiotech.eu

A total of 300 medicine and surgery clinical students completed the survey (170 from the University of Lagos and 130 from Lagos State University) resulting in a 40% response rate (calculated as the number of completed questionnaires divided by the potential number of eligible participants based on the MDCN quota for both colleges). The sociodemographic characteristics of the respondents by knowledge, ability and summary scores are shown in Table1. Respondents were 19 to 39 years old with a median age of 23 (IQR: 2224) and slightly higher females (52.3%). At least a quarter of the respondents were from each level, with the majority from sixth (38.3%) and fifth years (36.3%). Most respondents (63.3%) indicated an interest in a career involving research.

Most respondents (92.0%, n=276) indicated they had heard of at least one of the precision medicine terminologies. The most commonly indicated terminology were Pharmacogenomics (71.0%, n=213) and Genomic Medicine (47.7%, n=143), while the least indicated terminologies were Genome-guided prescribing (19.7%, n=59) and Next Generation Sequencing (18.0%, n=54). Among those who had indicated awareness, the most commonly cited source of knowledge was Lectures (49.6%, n=137), Media (34.4%, n=95) and less commonly Healthcare providers (10.1%, n=28) and Peers (5.1%, n=14).

Knowledge scores of the respondents ranged from 4 to 20, with a median knowledge score of 12 (IQR: 814.5). Respondents were more comfortable about their knowledge of genetic variations predisposing to common diseases (43.3%, n=130) and pharmacogenomics (38.0%, n=114). They were least comfortable about their understanding of basic genomic testing concepts and terminology (29.7%, n=89) and next-generation sequencing (23.3%, n=70). The distribution of responses to knowledge questions is shown in Fig.1.

Distribution of knowledge and ability responses of participants

On univariate analyses, respondents medical school year was significantly associated with their knowledge score (F [2,297]=3.23, p=0.04). Compared to those in their 4th year, students in their 6th year had a 1.54-point lower mean knowledge score (95%CI: -2.83, -0.24; p=0.02) while those in 5th year had a 0.39-point lower mean knowledge score but this was not statistically significant (95%CI: -1.69, 0.92; p=0.56). Students who indicated an interest in a career involving research had a borderline significant 1.03-point higher mean knowledge score compared to those who did not (95%CI: -0.03, 2.08; p=0.06). Age, gender and ethnicity of participants did not show any significant associations with knowledge score of the participants.

After sequentially adjusting for age, gender, and interest in a research career, participants medical school year was significantly associated with knowledge score (F [2, 294]=4.78, p=0.009). Students in their 6th year had a statistically significant 2.16-point lower mean knowledge score than those in their 4th year (95%CI: -3.60, -0.72; p=0.003). After adjusting for age, gender, and interest in a career involving research, each unit increase in medical school year was associated with a statistically significant 1.10-point lower mean knowledge score (F [1,295]=8.97, ptrend = 0.003) [Table2].

The ability scores of the respondents ranged from 4 to 20, with a median score of 11 (IQR: 715). Respondents were more comfortable about their ability to recommend genetic testing options to patients (39.0%, n=117), to a lesser extent, understand genomic test results (30.3%, n=91 and were least comfortable in their ability to make treatment recommendations based on genomic test results (29.3%, n=88) and explain genomic test results to patients (29.3%, n=88). The distribution of responses to ability questions is shown in Fig.1.

On univariate analyses, respondents medical school year was significantly associated with ability scores (F [2,297]=6.26, p=0.002). Compared to students in their 4th year, students in their 5th year had a statistically significant 1.47-point lower mean ability score (95%CI: -2.84, -0.09; p=0. 04) while students in their 6th year had a statistically significant 2.44-point lower mean ability score (95%CI: -3.81, -1.08; p<0.001). In addition, each unit increase in knowledge score was significantly associated with a 0.77-point increase in mean ability score (95%CI: 0.69, 0.86; p<0.001). Age, gender, ethnicity of participants and interest in a career involving research did not show any significant associations.

After multivariate adjustments for age, gender, medical school year, interest in a career involving research and knowledge score, participants knowledge score (: 0.76 95%CI: 0.67, 0.84; p<0.001), and medical school year (F [2,293]=4.67, p=0.01) were independent predictors of ability score. Compared to students in their 4th year, students in their 5th year had a 1.24-point lower mean ability score (95%CI: -2.21, -0.27; p=0.01), and those in their 6th year had a 1.58-point lower mean ability score (95%CI: -2.66, -0.50; p=0.004). After adjusting for age, gender, interest in a career involving research and knowledge score, each unit increase in medical school year was associated with a significant 0.78-point lower mean ability score (F [1,294]=8.06, ptrend = 0.005) [Table3].

The attitude scores of participants ranged from 14 to 40, with a median score of 28 (IQR: 2433). The median score on the openness items was 15 (IQR: 1216). Respondents were more willing to use a patients genetic information to guide decisions in clinical practice (62.0%, n=186), use new types of therapies to help patients (60.0%, n=180), and use genome-guided tools developed by researchers (56.0%, n-168) but were less willing to use genome-guided prescribing in their career when senior physicians were not (41.0%, n=123). The median score on the divergence items was 15 (IQR: 1217). Respondents agreed that research-based genome-guided interventions were clinically useful (79.0%, n=237), were willing to prescribe different medications or doses of drugs (61.0%, n=183), to a lesser extent disagreed that clinicians know how to treat patients based on their genetic information better than researchers (52.0%, n=156), and to a much lesser extent disagreed that clinical experience is more important than using a patients genetic information to make decisions (36.3%, n=109). The distribution of responses to attitude questions is shown in Fig.2.

Distribution of participants responses to attitudes questions

Respondents responses to questions assessing their attitudes towards the adoption of genome-guided prescribing and precision medicine. Section A includes the distribution of responses to openness questions while section B includes the distribution of responses to divergence questions

On univariate analyses, each unit increase in knowledge score of the participants was significantly associated with a 0.14 decrease in mean attitude score (95%CI: -0.26, -0.02; p=0.03). Age, gender, ethnicity, medical school year and interest in a career involving research were not significantly associated with attitude scores. Although the association with knowledge score persisted after adjusting for age and gender, adjusting for medical school year and interest in a career involving research resulted in a trend towards a null association. After maximal adjustment for age, gender, knowledge score, and interest in a research career, students in their 6th year had a significant 1.65-point higher mean attitude score than those in their 4th year (95%CI: 0.75, 3.23; p=0.04). However, medical school year overall was not significantly associated with attitude scores (F [2,293]=2.50, p=0.08). Nevertheless, after maximal adjustment, each unit increase in medical school year was significantly associated with a 0.81-point increase in mean attitude scores (95%CI: 0.02, 1.60; ptrend = 0.04) [Table4]. Likelihood ratio chi-square tests did not reveal any evidence of statistical interaction between knowledge scores and medical school year (X2=2.66, p=0.26).

The distribution of ethical concerns expressed by respondents is shown in Fig.3. More than a quarter of the respondents were worried that genomic information obtained would be misused by government and corporate bodies (35.7%, n=107) and that their application would increase margins between the rich and the poor (34.0%, n=102). A similar proportion were worried that results from tests can affect employability if serious genetic defects are made known to their employers (33.0%, n=99) and that they will lead to insurance discrimination (30.0%, n=90). However, less than a quarter of the respondents felt that precision medicine approaches would lead to ethnic/racial discrimination (12.3%, n=37), and only 8.7% (n=26) of the respondents felt that precision medicine approaches would violate privacy and confidentiality.

Respondents perceptions of ethical concerns and education about Precision Medicine

Most respondents (65.0%, n=195) thought it was important to learn about precision medicine. Only 11.3% (n=34) of the respondents felt that their education had adequately prepared them to practice precision medicine. Only 10.7% (n=32) thought they knew who to ask about genomic testing. Finally, only 10.3% (n=31) of the respondents felt their professors had encouraged the use of precision medicine. The distribution of responses to education items is shown in Fig.3.

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Perceptions of Nigerian medical students regarding their ... - BMC Medical Education

WEDNESDAY, Nov. 22, 2023 (HealthDay News) -- Publication bias inflates the apparent efficacy of alprazolam extended-release, according to a study published online Oct. 19 in Psychological Medicine.

Rosa Y. Ahn-Horst, M.D., M.P.H., from Massachusetts General Hospital in Boston, and Erick H. Turner, M.D., from the Veterans Affairs Portland Health Care System in Oregon, examined publication bias with alprazolam by comparing its efficacy for panic disorder using trial results from the published literature and the U.S. Food and Drug Administration. Data were included from all phase 2/3 efficacy trials of alprazolam extended-release (Xanax XR) for the treatment of panic disorder.

The researchers identified five trials in the FDA review, one of which had positive results (20 percent). Of the four trials without positive results, two were published conveying a positive outcome and two were not published. Therefore, according to the three published trials, 100 percent were positive. Using FDA data, alprazolam's effect size was 0.33 versus 0.47 using published data, representing a 42 percent increase.

"Clinicians are well aware of these safety issues, but there's been essentially no questioning of their effectiveness," Turner said in a statement. "Our study throws some cold water on the efficacy of this drug. It shows it may be less effective than people have assumed."

Abstract/Full Text

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Publication Bias Inflates Efficacy of Alprazolam XR: Study Reveals ... - HealthDay

How is pharmacogenomics affecting medical treatment?

Currently, doctors prescribe drugs based mostly on factors such as a patients age, weight, sex, and liver and kidney function. For a few drugs, researchers have identified gene variants that affect how people respond. In these cases, doctors can select the best medication and dose for each patient.

Additionally, learning how patients respond to medications helps to discern the different forms of their diseases.

For many years, NIH-funded scientists, through the Pharmacogenomics Research Network (PGRN), have studied the effect of genes on medications relevant to a wide range of conditions, including asthma, depression, cancer, and heart disease. The research findings are collected in an online resource called PharmGKB. In addition, the Clinical Pharmacogenetics Implementation Consortium (CPIC) was started as a shared partnership between the PGRN and PharmGKB to help lower the barrier to clinical use of pharmacogenetic tests. CPIC creates, curates, and posts freely available, peer-reviewed, evidence-based, updatable, and detailed gene/drug clinical practice guidelines. Another NIH-funded project, the Clinical Genome Resource, aims to define the clinical relevance of genes and variants for use in precision medicine and research.

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Pharmacogenomics - National Institute of General Medical ...

by Anand.K

The Human Genome Project is undoubtedly one of the most important and remarkable scientific feats in history and more recently, On March 31, 2022, the Telomere-to-Telomere (T2T) consortium announced that it had filled in the remaining gaps (roughly 8%) and produced the first truly complete human genome sequence. Highly accurate and long-read sequencing had finally removed technology limitations, enabling comprehensive studies of genomic variation across the entire human genome, which we expect to drive future discovery in human genomic health and disease.

continued below

Genomics applications in Rare Disorder Diagnosis Recently, a 19 month old child in the UK received lifesaving gene therapy for a rare disorder called metachromatic leukodystrophy. The ability to understand genomes quickly and inexpensively has led to advances in the diagnosis of rare disorders, thus helping families end their diagnostic odysseys. While rapid advances have been made in rare disorders, there are many disorders that may yet be discovered. Large scale research studies that are population specific would be required to understand the pattern of such diseases. This can provide significant relief to families with members suffering from such disorders, as well as help them understand the risk to future generations. 250+ such disorders have been identified. Through extensive genetic testing, it is possible to accelerate diagnosis and treatment options for patients living with rare diseases. Collaborations between public health organizations and private institutions, along with advocacy for mandatory new-born screening, can aid in reducing the inequalities that exist.

Prenatal testing to identify genetic disorders early in pregnancy Prenatal genetic tests required a pregnant woman to undergo invasive procedures to obtain a fetal DNA sample. Tests like amniocentesis and chorionic villus sampling comes with associated risks to pregnancy. With DNA sequencing, it is now possible to test the pregnant lady's blood for genomic variants in an unborn baby. NIPT (Non Invasive Prenatal Testing) or cell-free fetal DNA testing is now being extensively used to detect Down syndrome. With rapid advances in genomics, it is likely that we may be able to detect other genetic conditions very early.

In 2012, a new technique called CRISPR was invented that borrowed tools from bacteria to effectively edit any DNA in any organism. CRISPR is making it possible to edit genomes cheaper, faster, and more accurately than all previous methods. While CRISPR is now being used to study diseases, advances in this technology can also help in treating diseases. Research is being underway for Sickle Cell Disease and HIV. CRISPR has the potential to change gene therapy, and while it is still in its early stages, this could pave the way for new treatment options for a variety of life-threatening diseases.

The global genomics market size is projected to reach USD 94.65 billion by 2028, exhibiting a CAGR of 19.4% during the forecast period. While India is a land of 1.3 billion genomes and makes up 20% of the worlds population, the DNA sequences of our people only make up about 0.2% of global genetic databases. Therefore, we have a long way to go before we are able to reap the benefits of the genomics revolution at scale.

In the Union Budget 2022, Finance Minister Ms. Nirmala Sitharaman identified genomics as one of the sunrise opportunities and stated that the government will implement supportive policies to boost domestic capacities. Industry-academia collaborations, funding for research, and supportive regulatory and policy frameworks can truly transform how genomics can help healthcare delivery in India.

Anand.K, Chief Executive Officer, SRL Diagnostics

(DISCLAIMER: The views expressed are solely of the author and ETHealthworld does not necessarily subscribe to it. ETHealthworld.com shall not be responsible for any damage caused to any person / organisation directly or indirectly.)

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The Future of Genomics in India - ETHealthWorld

The pharmaceutical industry continues to be a hotbed of innovation, with activity driven by the evolution of new treatment paradigms, and the gravity of unmet needs, as well as the growing importance of technologies such as pharmacogenomics, digital therapeutics, and artificial intelligence. In the last three years alone, there have been over 633,000 patents filed and granted in the pharmaceutical industry, according to GlobalDatas report on Immuno-oncology in Pharmaceuticals: Microbiota restoration therapy.

According to GlobalDatas Technology Foresights, which uses over 756,000 patents to analyse innovation intensity for the pharmaceutical industry, there are 110 innovation areas that will shape the future of the industry.

Microbiota restoration therapy is a key innovation area in immuno-oncology

Microbiota restoration therapy can be composed of human faecal material containing viable gut flora from a patient or donor, and include a diluent and a cryoprotectant. The human faecal material is screened before using it in the restoration therapy for any pathogenic microorganisms.

GlobalDatas analysis also uncovers the companies at the forefront of each innovation area and assesses the potential reach and impact of their patenting activity across different applications and geographies. According to GlobalData, there are 240+ companies, spanning technology vendors, established pharmaceutical companies, and up-and-coming start-ups engaged in the development and application of microbiota restoration therapy.

Key players in microbiota restoration therapy a disruptive innovation in the pharmaceutical industry

Application diversity measures the number of different applications identified for each relevant patent and broadly splits companies into either niche or diversified innovators.

Geographic reach refers to the number of different countries each relevant patent is registered in and reflects the breadth of geographic application intended, ranging from global to local.

Source: GlobalData Patent Analytics

Probiotical is the leading patent filer for microbiota restoration therapy. Probiotical is a manufacturer of probiotics and synbiotics products. The companys activities consist of several stages of research and development, strain isolation, characterisation and production of probiotic strains for the prevention and treatment of various diseases, and design and implementation of specific probiotics and synbiotics finished products in many therapeutic areas, supported by clinical studies.

In terms of application diversity, Fate Therapeutics is the top company, followed by Imstem Biotechnology and the Spanish National Research Council. By means of geographic reach, the Spanish National Research Council holds the top position. While GI Innovation and Vitacare stand in second and third positions, respectively.

To further understand the key themes and technologies disrupting the pharmaceutical industry, access GlobalDatas latest thematic research report on Pharmaceutical.

Get industry leading news, data and analysis delivered to your inbox

GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article.

GlobalDatas Patent Analytics tracks patent filings and grants from official offices around the world. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the worlds largest industries.

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Who are the leading innovators in microbiota restoration therapy for ... - Pharmaceutical Technology

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Effect of pharmacogenomics testing guiding on clinical outcomes in ... - BMC Psychiatry

Understanding pharmacogenomics would not be possible without sequencing the genomes of many people and comparing them, and then comparing their response to medicines. But we have also learned that a person's genome sequence is not everything when it comes to medication responses. The human body is a very complicated machine, and the instructions written in our DNA are just part of the process.

There are some cases, as with the breast cancer treatment tamoxifen, where a small study showed that there might be a relationship between someone's response to the medicine and a variant in theCYP2D6gene. However, this finding did not appear to be true in a larger study that involved many more people. That's why at this time, the U.S. Food and Drug Administration (FDA) labeling for tamoxifen does not recommendCYP2D6pharmacogenomic testing, butthe issue is still being reviewedas more research is conducted.

Another gene in the sameCYPfamily, calledCYP2C19, has variations which affect how your body can useclopidogrel(more commonly known as Plavix). This medication is a "blood thinner" which helps prevent blood clots, and thus reduces your risk of strokes or some heart attacks. If yourCYP2C19protein is not working properly due to a mutation in the gene, then you will not be able toprocess clopidogrel, and you need either a different dose or a different medication. As it turns out, these variants inCYP2C19are also more common in those with Asian ancestry. Although testing for variants in this gene is also not routinely recommended, you may wish to speak with your healthcare provider about the test if you are given a prescription for clopidogrel, particularly if you have East Asian family members.

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Pharmacogenomics - Genome.gov

Molecular Diagnostics Market is estimated to be US$ 45.30 billion by 2030 with a CAGR of 11.5% - By PMI  EIN News

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Molecular Diagnostics Market is estimated to be US$ 45.30 billion by 2030 with a CAGR of 11.5% - By PMI - EIN News

In the 1970s, NIH research gave us genetic engineering and launched what is today the $100 billion biotechnology industry, a major source of high-paying U.S. jobs. Virtually every biomedical research lab and pharmaceutical company uses the power of the genomic revolution every day to demystify diseases and search for new cures. Companies today can read the entire DNA sequence of an individual for less than $1,000, and the cost is dropping quickly. This ability to study massive amounts of DNA has helped the field of pharmacogenomics mature rapidly. In this area of science, researchers match DNA patterns in individuals with how they respond to medications. The goal is to move away from one-size-fits-all dosing because we now know that many factors aside from sex, age, and body size influence how our bodies react, ordont, to many drugs. Research results in this important area of biomedicine have prompted FDA to include pharmacogenomic information in drug labeling, toward more precise and safer drug responses for patients. A significant goal of precision medicine is to implement this strategy broadly in medical care focusing on the right drug at the right dose at the right time for the right patient.

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Pharmacogenomics | National Institutes of Health (NIH)

Proteomics

The suffix "-ome" comes from the Greek for all, every, or complete. It was originally used in "genome," which refers to all the genes in a person or other organism. Due to the success of large-scale biology projects such as the sequencing of the human genome, the suffix "-ome" is now being used in other research contexts. Proteomics is an example. The DNA sequence of genes carries the instructions, or code, for building proteins. This DNA is transcribed into a related molecule, RNA, which is then translated into proteins. Proteomics, therefore, is a similar large-scale analysis of all the proteins in an organism, tissue type, or cell (called the proteome). Proteomics can be used to reveal specific, abnormal proteins that lead to diseases, such as certain forms of cancer.

Pharmacogenetics and Pharmacogenomics

The terms "pharmacogenetics" and "pharmacogenomics" are often used interchangeably in describing the intersection of pharmacology (the study of drugs, or pharmaceuticals) and genetic variability in determining an individual's response to particular drugs. The terms may be distinguished in the following way.

Pharmacogenetics is the field of study dealing with the variability of responses to medications due to variation in single genes. Pharmacogenetics takes into account a person's genetic information regarding specific drug receptors and how drugs are transported and metabolized by the body. The goal of pharmacogenetics is to create an individualized drug therapy that allows for the best choice and dose of drugs. One example is the breast cancer drug trastuzumab (Herceptin). This therapy works only for women whose tumors have a particular genetic profile that leads to overproduction of a protein called HER2. (See: Genetics, Disease Prevention and Treatment)

Pharmacogenomics is similar to pharmacogenetics, except that it typically involves the search for variations in multiple genes that are associated with variability in drug response. Since pharmacogenomics is one of the large-scale "omic" technologies, it can examine the entirety of the genome, rather than just single genes. Pharmacogenomic studies may also examine genetic variation among large groups of people (populations), for example, in order to see how different drugs might affect different racial or ethnic groups.

Pharmacogenetic and pharmacogenomic studies are leading to drugs that can be tailor-made for individuals, and adapted to each person's particular genetic makeup. Although a person's environment, diet, age, lifestyle, and state of health can also influence that person's response to medicines, understanding an individual's genetic makeup is key to creating personalized drugs that work better and have fewer side effects than the one-size-fits-all drugs that are common today. (See: Genetics, Disease Prevention and Treatment). For example, the U.S. Food and Drug Administration (FDA) recommends genetic testing before giving the chemotherapy drug mercaptopurine (Purinethol) to patients with acute lymphoblastic leukemia. Some people have a genetic variant that interferes with their ability to process this drug. This processing problem can cause severe side effects, unless the standard dose is adjusted according to the patient's genetic makeup. (See: Frequently Asked Questions about Pharmacogenomics).

Stem Cell Therapy

Stem cells have two important characteristics. First, stem cells are unspecialized cells that can develop into various specialized body cells. Second, stem cells are able to stay in their unspecialized state and make copies of themselves. Embryonic stem cells come from the embryo at a very early stage in development (the blastocyst staqe). The stem cells in the blastocyst go on to develop all of the cells in the complete organism. Adult stem cells come from more fully developed tissues, like umbilical cord blood in newborns, circulating blood, bone marrow or skin.

Medical researchers are investigating the use of stem cells to repair or replace damaged body tissues, similar to whole organ transplants. Embryonic stem cells from the blastocyst have the ability to develop into every type of tissue (skin, liver, kidney, blood, etc.) found in an adult human. Adult stem cells are more limited in their potential (for example, stem cells from liver may only develop into more liver cells). In organ transplants, when tissues from a donor are placed into the body of a patient, there is the possibility that the patient's immune system may react and reject the donated tissue as "foreign." However, by using stem cells, there may be less risk of this immune rejection, and the therapy may be more successful.

Stem cells have been used in experiments to form cells of the bone marrow, heart, blood vessels, and muscle. Since the 1990's, umbilical cord blood stem cells have been used to treat heart and other physical problems in children who have rare metabolic conditions, or to treat children with certain anemias and leukemias. For example, one of the treatment options for childhood acute lymphoblastic leukemia [cancer.gov] is stem cell transplantation therapy.

There has been much debate nationally about the use of embryonic stem cells, especially about the creation of human embryos for use in experiments. In 1995, Congress enacted a ban on federal financing for research using human embryos. However, these restrictions have not stopped researchers in the United States and elsewhere from using private funding to create new embryonic cell lines and undertaking research with them. The embryos for such research are typically obtained from embryos that develop from eggs that have been fertilized in vitro - as in an in vitro fertilization clinic - and then donated for research purposes with informed consent of the donors. In 2009, some of the barriers to federal financing of responsible and scientifically worthy human stem cell research were lifted.

Cloning

Cloning can refer to genes, cells, or whole organisms. In the case of a cell, a clone refers to any genetically identical cell in a population that comes from a single, common ancestor. For example, when a single bacterial cell copies its DNA and divides thousands of times, all of the cells that are formed will contain the same DNA and will be clones of the common ancestor bacterial cell. Gene cloning involves manipulations to make multiple identical copies of a single gene from the same ancestor gene. Cloning an organism means making a genetically identical copy of all of the cells, tissues, and organs that make up the organism. There are two major types of cloning that may relate to humans or other animals: therapeutic cloning and reproductive cloning.

Therapeutic cloning involves growing cloned cells or tissues from an individual, such as new liver tissue for a patient with a liver disease. Such cloning attempts typically involve the use of stem cells. The nucleus will be taken from a patient's body cell, such as a liver cell, and inserted into an egg that has had its nucleus removed. This will ultimately produce a blastocyst whose stem cells could then be used to create new tissue that is genetically identical to that of the patient.

Reproductive cloning is a related process used to generate an entire animal that has the same nuclear DNA as another currently or previously existing animal. The first cloned animals were frogs. Dolly, the famous sheep, is another example of cloning. The success rates of reproductive animal cloning, however, have been very low. In 2005, South Korean researchers claimed to have produced human embryonic stem cell lines by cloning genetic material from patients. However, this data was later reported to have been falsified.

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Genetics vs. Genomics Fact Sheet - National Human Genome Research Institute

Personalized medicine tailors therapies, disease prevention, and health maintenance to the individual, with pharmacogenomics serving as a key tool to improve outcomes and prevent adverse effects. Advances in genomics have transformed pharmacogenetics, traditionally focused on single gene-drug pairs, into pharmacogenomics, encompassing all 'omics' fields, e.g., proteomics, transcriptomics, metabolomics, and metagenomics. This review summarizes basic genomics principles relevant to translation into therapies, assessing pharmacogenomics' central role in converging diverse elements of personalized medicine. We discuss genetic variations in pharmacogenes (drug-metabolizing enzymes, drug transporters, and receptors), their clinical relevance as biomarkers, and the legacy of decades of research in pharmacogenetics. All types of therapies, including proteins, nucleic acids, viruses, cells, genes, and irradiation, can benefit from genomics, expanding the role of pharmacogenomics across medicine. FDA approvals of personalized therapeutics involving biomarkers increase rapidly, demonstrating the growing impact of pharmacogenomics. A beacon for all therapeutic approaches, molecularly targeted cancer therapies highlight trends in drug discovery and clinical applications. To account for human complexity, multi-component biomarker panels encompassing genetic, personal, and environmental factors can guide diagnosis and therapies, increasingly involving artificial intelligence to cope with extreme data complexities. However, clinical application encounters substantial hurdles, such as unknown validity across ethnic groups, underlying bias in health care, and real-world validation. This review will address the underlying science and technologies germane to pharmacogenomics and personalized medicine, integrated with economic, ethical, and regulatory issues - providing insights into the current status and future direction of health care. Significance Statement Personalized medicine aims to optimize health care for the individual patients with use of predictive biomarkers to improve outcomes and prevent adverse effects. Pharmacogenomics drives biomarker discovery and guides the development of targeted therapeutics. This review addresses basic principles and current trends in pharmacogenomics, with large-scale data repositories accelerating medical advances. The impact of pharmacogenomics is discussed, along with hurdles impeding broad clinical implementation, in the context of clinical care, ethics, economics, and regulatory affairs.

Keywords: Genetic polymorphisms; cancer; developmental pharmacology; drug metabolism; drug-drug interactions; gene regulation/transcription; pharmacogenetics/pharmacogenomics; systems pharmacology.

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PHARMACOGENOMICS: Driving Personalized Medicine

About the program

The Pharmaceutical Sciences and Pharmacogenomics (PSPG) Graduate Program at the University of California, San Francisco (UCSF) focuses on how to develop effective drug therapies for patients that have a minimum of adverse effects. To do this we give our graduate students solid training in the pharmaceutical-related basic sciences and create an environment in which students can develop into independent and creative scientific problem-solvers. This multidisciplinary graduate program has a dual focus: pharmaceutical sciences and drug development, and pharmacogenomics, which is the application of genetics and genomics to drug action and disposition. The result of this dual focus is that it trains the next generation of scientists to explore new drugs in novel ways.

PSPG welcomes scientists of any race, religion, national origin, gender identity, caregiver and family commitments, political affiliation, sexual orientation, and eligible age or ability. We believe Black Lives Matter and are committed to sustained action to reduce racism and inequity in science. More details:Diversity, Equity, and Inclusion.

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StudiesBrexpiprazolePsychiatryCYP2D6Dosage and Administration, Use in Specific Populations, Clinical PharmacologyBrigatinibOncologyALKIndications and Usage, Dosage and Administration, Adverse Reactions, Clinical StudiesBrivaracetamNeurologyCYP2C19Clinical PharmacologyBupivacaine (1) AnesthesiologyG6PDWarningsBupivacaine (2)AnesthesiologyNonspecific(Congenital Methemoglobinemia)WarningsBupropionPsychiatryCYP2D6Clinical PharmacologyBusulfanOncologyBCR-ABL1(Philadelphia chromosome)Clinical StudiesCabotegravir and Rilpivirine (1)Infectious DiseasesHLA-BClinical StudiesCabotegravir and Rilpivirine (2)Infectious DiseasesUGT1A1Clinical PharmacologyCabozantinibOncologyRETClinical StudiesCapmatinibOncologyMETIndications and Usage, Dosage and Administration, Clinical StudiesCapecitabineOncologyDPYDWarnings and Precautions, Patient Counseling InformationCarbamazepine (1)NeurologyHLA-BBoxed Warning, Warnings, PrecautionsCarbamazepine (2)NeurologyHLA-AWarningsCarglumic AcidInborn Errors of MetabolismNAGSIndications and Usage, Dosage and Administration, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesCariprazinePsychiatryCYP2D6Clinical PharmacologyCarisoprodolRheumatologyCYP2C19Use in Specific Populations, Clinical PharmacologyCarvedilolCardiologyCYP2D6Drug Interactions, Clinical PharmacologyCasimersenNeurologyDMDIndications and Usage, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesCeftriaxone (1)Infectious DiseasesG6PDWarningsCeftriaxone (2)Infectious DiseasesNonspecific(Congenital Methemoglobinemia)WarningsCelecoxibRheumatologyCYP2C9Dosage and Administration, Use in Specific Populations, Clinical PharmacologyCemiplimab-rwlc (1)OncologyALKIndications and Usage, Clinical StudiesCemiplimab-rwlc (2)OncologyCD274(PD-L1)Indications and Usage, Dosage and Administration, Clinical StudiesCemiplimab-rwlc (3)OncologyEGFRIndications and Usage, Clinical StudiesCemiplimab-rwlc (4)OncologyROS1Indications and Usage, Clinical StudiesCeritinibOncologyALKIndications and Usage, Dosage and Administration, Warning and Precautions, Adverse Reactions, Clinical StudiesCerliponase AlfaInborn Errors of MetabolismTPP1Indications and Usage, Use in Specific Populations, Clinical StudiesCetuximab (1)OncologyEGFRIndications and Usage, Dosage and Administration, Adverse Reactions, Clinical StudiesCetuximab (2)OncologyRASIndications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Clinical StudiesCetuximab (3)OncologyBRAFIndications and Usage, Dosage and Administration, Adverse Reactions, Use in Specific Populations, Clinical StudiesCevimelineDentalCYP2D6PrecautionsChloroprocaine (1)AnesthesiologyG6PDWarningsChloroprocaine (2)AnesthesiologyNonspecific(Congenital Methemoglobinemia)WarningsChloroquineInfectious DiseasesG6PDPrecautions, Adverse ReactionsChlorpropamideEndocrinologyG6PDPrecautionsCholic AcidInborn Errors of MetabolismAMACR, AKR1D1, CYP7A1, CYP27A1, DHCR7, HSD3B2(Bile Acid Synthesis Disorders)Indications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Use in Specific Populations, Clinical StudiesCisplatinOncologyTPMTAdverse ReactionsCitalopram (1)PsychiatryCYP2C19Dosage and Administration, Warnings,Clinical PharmacologyCitalopram (2)PsychiatryCYP2D6Clinical PharmacologyClobazamNeurologyCYP2C19Dosage and Administration, Use in Specific Populations, Clinical PharmacologyClomipraminePsychiatryCYP2D6PrecautionsClopidogrelCardiologyCYP2C19Boxed Warning, Warnings and Precautions, Clinical PharmacologyClozapinePsychiatryCYP2D6Dosage and Administration, Use in Specific Populations, Clinical PharmacologyCobimetinibOncologyBRAFIndications and Usage, Dosage and Administration, Adverse Reactions, Clinical StudiesCodeineAnesthesiologyCYP2D6Boxed Warning, Warnings and Precautions, Use in Specific Populations, Patient Counseling InformationCrizanlizumab-tmcaHematologyHBBAdverse Reactions, Clinical StudiesCrizotinib (1)OncologyALKIndications and Usage, Dosage and Administration, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesCrizotinib (2)OncologyROS1Indications and Usage, Dosage and Administration, Adverse Reactions, Use in Specific Populations, Clinical StudiesDabrafenib (1)OncologyBRAFIndications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesDabrafenib (2)OncologyG6PDWarnings and Precautions, Adverse Reactions, Patient Counseling InformationDabrafenib (3)OncologyRASDosage and Administration, Warnings and PrecautionsDaclatasvirInfectious DiseasesIFNL3(IL28B)Clinical StudiesDacomitinibOncologyEGFRIndications and Usage, Dosage and Administration, Adverse Reactions, Use in Specific Populations, Clinical StudiesDapsone (1)DermatologyG6PDWarnings and Precautions, Use in Specific Populations, Patient Counseling InformationDapsone (2)DermatologyNonspecific(Congenital Methemoglobinemia)Warnings and Precautions, Adverse Reactions, Patient Counseling InformationDapsone (3)Infectious DiseasesG6PDPrecautions, Adverse Reactions, OverdosageDarifenacinUrologyCYP2D6Clinical PharmacologyDasabuvir, Ombitasvir, Paritaprevir, andRitonavirInfectious DiseasesIFNL3(IL28B)Clinical StudiesDasatinibOncologyBCR-ABL1(Philadelphia chromosome)Indications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Use in Specific Populations, Clinical StudiesDenileukin DiftitoxOncologyIL2RA(CD25 antigen)Indications and Usage, Clinical StudiesDesipraminePsychiatryCYP2D6PrecautionsDesfluraneAnesthesiologyNonspecific(Genetic Susceptibility to Malignant Hyperthermia)ContraindicationsDesvenlafaxinePsychiatryCYP2D6Clinical PharmacologyDeutetrabenazineNeurologyCYP2D6Dosage and Administration, Warnings and Precautions, Use in Specific Populations, Clinical PharmacologyDexlansoprazoleGastroenterologyCYP2C19Drug Interactions, Clinical PharmacologyDextromethorphan and QuinidineNeurologyCYP2D6Warnings and Precautions, Clinical PharmacologyDiazepamNeurologyCYP2C19Clinical PharmacologyDinutuximabOncologyMYCNClinical StudiesDocetaxelOncologyESR, PGR(Hormone Receptor)Clinical StudiesDolutegravirInfectious DiseasesUGT1A1Clinical PharmacologyDonepezilNeurologyCYP2D6Clinical PharmacologyDostarlimab-gxlyOncologyMismatch RepairIndication and Usage, Dosage and Administration, Adverse Reactions, Clinical StudiesDoxepin (1)PsychiatryCYP2D6Clinical PharmacologyDoxepin (2)PsychiatryCYP2C19Clinical PharmacologyDronabinolGastroenterologyCYP2C9Use in Specific Populations, Clinical PharmacologyDrospirenone and Ethinyl EstradiolGynecologyCYP2C19Clinical PharmacologyDuloxetinePsychiatryCYP2D6Drug InteractionsDurvalumabOncologyCD274(PD-L1)Clinical Pharmacology, Clinical StudiesDuvelisibOncologyChromosome 17pClinical StudiesEculizumab (1)NeurologyACHRIndications and Usage, Clinical StudiesEculizumab (2)NeurologyAQP4Indications and Usage, Clinical StudiesEfavirenzInfectious DiseasesCYP2B6Clinical PharmacologyEfgartigimod Alfa-fcabNeurologyACHRIndications and Usage, Clinical Pharmacology, Clinical StudiesElagolixGynecologySLCO1B1Clinical PharmacologyElbasvir and GrazoprevirInfectious DiseasesIFNL3(IL28B)Clinical StudiesElexacaftor, Ivacaftor, and TezacaftorPulmonaryCFTRIndications and Usage, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesEliglustatInborn Errors of MetabolismCYP2D6Indications and Usage, Dosage and Administration, Contraindications, Warnings and Precautions, Drug Interactions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesElosulfaseInborn Errors of MetabolismGALNSIndications and Usage, Warnings and Precautions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesEltrombopag (1)HematologyF5(Factor V Leiden)Warnings and PrecautionsEltrombopag (2)HematologySERPINC1(Antithrombin III)Warnings and PrecautionsEltrombopag (3)HematologyChromosome 7Adverse ReactionsEltrombopag (4)HematologyChromosome 13Adverse ReactionsEmapalumab-lzsgHematologyPRF1, RAB27A, SH2D1A, STXBP2, STX11, UNC13D, XIAP (Hemophagocytic Lymphohistiocytosis)Clinical StudiesEnasidenibOncologyIDH2Indications and Usage, Dosage and Administration, Clinical Pharmacology, Clinical StudiesEncorafenib (1)OncologyBRAFIndications and Usage, Dosage and Administration, Warnings and Precautions, Adverse Reactions, Use in Specific Populations, Clinical Pharmacology, Clinical StudiesEncorafenib (2)OncologyRASDosage and Administration, Warnings and Precautions, Clinical StudiesEnfortumab Vedotin-ejfvOncology

NECTIN4

SERPINC1(Antithrombin III)

ERBB2(HER2)

Indications and Usage, Adverse Reactions, Clinical Studies

Clinical Studies

Indications and Usage, Clinical Studies

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Table of Pharmacogenomic Biomarkers in Drug Labeling | FDA