The serotonin transporter (SLC6A4) and the serotonin autoreceptor (HTR1A) are two of the most extensively studied genes in the field of psychiatry, and their variants have been implicated in antidepressant response, specifically with selective serotonin reuptake inhibitors (SSRIs) which are widely regarded as the first-line medications for depression and anxiety. Variants of SLC6A4 and HTR1A have also been studied as risk factors for depression. In this retrospective study, we aim to investigate the relationship between all possible serotonin transporter (SLC6A4) and autoreceptor (HTR1A) variant expression combinations that may have contributed to the therapeutic failure of an SSRI and subsequent disability. In this study, we utilize data from a cohort of 302 European patients diagnosed with depression and/or anxiety who were referred to Personalized Prescribing Inc. (PPI) in 2022 as result of a mental health disability claim to determine whether statistical differences are present in this cohort as compared to general European population allele frequencies. Our data reveals the presence and relevance of significant differences in the presentation of SLC6A4 and HTR1A, specifically in a disability cohort, relative to the average European population. The SLC6A4 gene codes for the serotonin transporter; the SSRI drug target that aims to be blocked to prevent the recycling of serotonin, whereas the HTR1A plays an indirect role as an autoreceptor allowing serotonin levels to be maintained by the SSRI, as well as a direct role in modulating mood through post-synaptic serotonin interaction. This study has revealed statistically significant differences in the expression of these two genes together in increasing the likelihood of drug failure, specifically the presence of one or more G alleles at HTR1A rs6295 in combination with the SLC6A4 SS variant. The most significantly overrepresented combination in this cohort of patients suffering from depression and anxiety that have failed to achieve adequate symptom remission on previous SSRI trials is HTR1A rs6295 GG-SLC6A4 SS which is overrepresented in this study by over 74% at a p-value well below 0.01. Genotyping anti-depressant drug targets may play an important role in optimizing anti-depressant drug response and research developments for future therapies.
The aim of this study was to elucidate cardiovascular prescriber access, uptake, and attitudes toward CYP2C19 and CYP2D6 genetic testing to guide prescribing of commonly used medications such as clopidogrel, antiarrhythmics, proton pump inhibitors, and antidepressants. A survey, designed in collaboration with the European Society of Cardiology (ESC) WG on Cardiovascular Pharmacotherapy and external experts was disseminated to ESC members using SurveyMonkey. 265 prescribers from 68 countries participated. Most respondents thought testing would be beneficial, though CYP2C19 testing was perceived as more beneficial (73%) and desirable than CYP2D6 (61%). Access to CYP2C19 testing was more common (30%) than CYP2D6 testing (19%), but mostly outside of public funded health systems. Uptake in those who had access was higher for CYP2C19 (67%), than for CYP2D6 (33%). Confidence in interpreting results to prescribe was also higher with CYP2C19 (69%) than with CYP2D6 (53%), but most respondents wanted information prior to prescribing. One third of respondents highlighted the need for a turnaround time that matched their clinical practice. Unsolicited Pharmacogenomic (PGx) information from a patient was uncommon, but most prescribers acted on the information. A minority of respondents had undertaken PGx testing themselves, but most wanted testing for relevant medications. Respondents' experiences as patients made them more likely to believe that PGx testing was warranted. A minority ( ~ 15%) were aware of either local prescriber guidance or patient information materials regarding PGx testing. Prescribers want access to pharmacogenomics data regarding CYP2C19 and CYP2D6 for prescribing cardiovascular medicines. However, there are barriers which hamper implementation. Prescribers lived experience with medication use as patients impacted their views of PGx. 265 prescribers responded to the ESC survey from 68 countries. Most prescribers wanted access to pharmacogenomic testing for CYP2C19 and CYP2D6 for their patients and for themselves. Though most prescribers thought these pharmacogenomic tests would be useful and could improve the risk/benefit profile of relevant medications, prescribers responded more positively to CYP2C19 compared with CYP2D6 testing. Guidance and information for both prescribers and patients were lacking.
Acute leukemias are highly aggressive hematologic malignancies that demand intensive chemotherapy regimens. However, drug toxicity remains a major barrier to treatment success and patient survival. In this context, pharmacogenomics offers a promising strategy by identifying single-nucleotide variants (SNVs) that influence drug metabolism, efficacy, and toxicity, ultimately impacting treatment outcomes. This study analyzed data from the ClinPGx/PharmGKB database to identify clinically annotated variants related to chemotherapy response in Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL). A total of 24 variants were curated for AML and 57 for ALL. Among these, nonsynonymous variants were most frequent in ALL (31.6%), while synonymous variants predominated in AML (33.3%). Although traditionally considered neutral, synonymous and intronic variants may influence gene expression through regulatory or splicing mechanisms. The analysis revealed clinically significant variants associated with chemotherapy response, particularly in the ABCB1 gene, observed in 12.5% of AML and 10.5% of ALL cases. Several variants, particularly TPMT, NUDT15, ABCC1, SLC28A3, and RARG, were associated with severe adverse effects such as myelotoxicity, mucositis, cardiotoxicity, and hepatotoxicity. This study reinforces the importance of genetic variants in modulating the therapeutic response and toxicity to chemotherapy drugs in acute leukemias. Analysis of ClinPGx/PharmGKB data emphasizes ABCB1 as a potential resistance marker and supports pre-treatment genotyping of genes like TPMT and NUDT15 to prevent severe toxicities. Future advances should include the expansion of pharmacogenetic studies in underrepresented populations and the clinical validation of new markers in prospective trials, aiming to consolidate precision medicine as a routine part of the therapeutic management of acute leukemias.
Maternal and neonatal health (MNH) urgently requires precision medicine interventions, as morbidity, mortality, and health disparities hinder the achievement of Sustainable Development Goal 3. Clinical implementation of artificial intelligence (AI)-powered Pharmacogenomics (PGx) requires validated, transparent algorithms and frameworks. The "pregnancy black box"-which refers to a data void due to historical exclusion of pregnant and postpartum women from clinical trials-continues to create bias in AI models. The review establishes a path for upcoming research, including methods to reduce algorithmic bias via AI-driven data augmentation, resolution of ethical challenges, and creation of international registries. Ultimately, leveraging AI for remote monitoring is crucial for enhancing equitable access in lower-resource environments. The proposed roadmap provides organizations with a robust framework to develop AI-driven PGx systems, which will enable safer and more tailored pharmacotherapy for mothers and their newborns.
Intensive care units (ICU) patients are highly vulnerable to inaccurate drug dosing. Pharmacogenomics (PGx) studies the role of inherited genetic variation in drug metabolism and dose efficacy. To assess the prevalence of PGx variants that may influence therapeutic effect in the ICU, we carried out whole genome sequencing (WGS) of 210 Qataris in ICU care at Hamad Medical Corporation (HMC), Doha, Qatar and assessed the WGS for predicted deleterious variants of genes that metabolize 30 drugs commonly prescribed in the ICU. PGx variation was evaluated using two complementary approaches. First, variants with established functional interpretation were assessed using CPIC guidelines and star-allele haplotypes inferred by PharmCAT to estimate the prevalence of alleles associated with abnormal drug metabolism. Second, a broader exploratory analysis examined computationally predicted deleterious single-nucleotide variants in pharmacogenes that currently lack CPIC guidelines or defined star alleles, with these findings interpreted as descriptive of genomic variation rather than clinical metabolizer phenotypes.Of the ICU patients that received the 5 most commonly prescribed drugs (warfarin, phenytoin, midazolam, vancomycin, levetiracetam), 93% had deleterious metabolism-related variants. Ninety-one % of ICU patients carried at least one variant in a gene with known PGx relevance that could potentially impact the metabolism or activity of at least one medication they received. Most patients had ≥14 deleterious variants of genes that affect the metabolism of administered drugs. Comparison of the deleterious variants related to metabolism of ICU drugs with African/African American and European populations revealed significant population specificity in ICU related PGx variants. Together, these data suggest that population specific, PGx based on the individual's genome likely plays a significant role in effective, safe dosing in the ICU setting.
Pharmacogenomics (PGx)-guided prescribing is a promising approach to reduce variability in drug response, although its cost-effectiveness remains uncertain. We performed a systematic review and meta-analysis evaluating the cost-effectiveness of PGx-guided prescribing compared to standard care in psychiatry. In January 2026, we searched MEDLINE, Embase and PsycINFO for studies published between 2014 and 2025. We included any peer-reviewed study that included adults with a diagnosed psychiatric disorder, comparing PGx-guided prescribing to standard care, and reported both quality-of-life and economic outcomes. Given the lack of consensus on synthesising economic evidence, both a narrative synthesis and meta-analysis were conducted. Pooled incremental net benefit (INB) was used as the effect measure for the meta-analysis and heterogeneity measures including the I2 test were used to assess heterogeneity and determine which model to use for the meta-analysis. From an initial 1 271 records, 17 studies were included. The narrative synthesis found that 88% of studies favoured PGx-guided prescribing. Meta-analyses produced positive, though non-significant, pooled Incremental Net Benefits (INBs) for the total study groups (£1 623.14, 95% CI: -£116.50 to £3 362.79, p = 0.07, I2 = 100%), and for a statistically homogeneous subgroup (£41.54, 95% CI: -£18.27 to £101.35, p = 0.17, I2 = 0%). Our review indicates that PGx-guided prescribing can be cost-effective in psychiatry but highlights the need for increased consensus in economic modelling methods.
Tumor immune cell infiltration plays an important role in determining treatment response and prognosis in colorectal cancer (CRC). This study aimed to investigate the association between tumor immune landscape and clinical outcomes in CRC patients receiving infusional 5-fluorouracil/leucovorin combined with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI)-based chemotherapy. Immune cell infiltration profiles were evaluated using transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the relative proportions of 22 immune cell subtypes were estimated using the CIBERSORTx algorithm. Associations between immune cell infiltration and treatment response, progression-free survival (PFS), and overall survival (OS) were systematically analyzed. A total of 511 CRC patients were included in the analysis. In patients receiving FOLFOX chemotherapy, improved drug response rates were positively associated with increased infiltration of M1 macrophages, whereas higher levels of gamma delta (γδ) T cells were correlated with poorer treatment response and reduced OS. In patients treated with FOLFIRI, elevated infiltration of M1 macrophages, M2 macrophages, activated natural killer (NK) cells, and T follicular helper (Tfh) cells was associated with unfavorable OS. Consensus clustering analysis identified three distinct immune subtypes, among which one subtype exhibited superior drug response rates and improved clinical outcomes following FOLFIRI treatment and was characterized by enrichment of adaptive immune cells, particularly memory CD4⁺ T cells and B cell-related populations. These findings demonstrate that specific immune cell subtypes and immunologically defined tumor subgroups are significantly associated with chemotherapy response and survival outcomes in CRC patients, highlighting the potential of tumor immune profiling as a predictive biomarker for chemotherapy efficacy.
Cardiovascular diseases are the leading cause of death in Chile and worldwide, representing a major public health challenge that demands urgent preventive and therapeutic strategies. In atrial fibrillation, anticoagulation is essential, and in Chile acenocoumarol rather than warfarin, used in most countries, is the standard agent. Its dosing shows substantial interindividual variability due to CYP2C9 and VKORC1 polymorphisms. We developed a cohort-based Markov model to compare standard care, genotype-guided dosing, and genotype-guided dosing adjusted for population-level adherence in 123 Chilean patients with atrial fibrillation and 123 matched simulated individuals. Outcomes were measured as quality-adjusted life years (QALYs) and direct medical costs, with cost-effectiveness assessed at a willingness-to-pay (WTP) threshold of US$17,093, estimated using the international approach of approximating the country's GDP per capita rather than a Chilean policy-based value. Genotype-guided dosing achieved the highest effectiveness (2938.34 QALYs) with an incremental cost-effectiveness ratio of US$436.86/QALY versus standard care, remaining cost-effective in sensitivity analyses up to test prices far exceeding the current US$190. The adherence-adjusted strategy was weakly dominated. These results strongly support implementing pharmacogenetic testing for acenocoumarol dosing to optimize anticoagulation safety, efficacy, and cost-effectiveness in Chile.
Genetic variation in CYP2C19 is linked to variable efficacy in antiplatelet therapies like clopidogrel. Patients with CYP2C19*2 and *3 loss of function alleles show reduced enzyme function, leading to lower active drug levels and higher risks of thromboembolic and cardiovascular events. Point-of-care CYP2C19 testing offers a personalized approach to antiplatelet therapy. We conducted a systematic review of literature assessing point-of-care CYP2C19 testing to individualize antiplatelet therapy in cardiovascular patients compared to standard therapies. The review followed PRISMA guidelines and identified 146 articles, of which 3 randomized controlled trials (RCTs) were comprised. The three studies included 6945 patients; 3483 received genotype-guided therapy, and 3462 received standard care. Efficacy and safety outcomes were compared between groups. Point-of-care genotype-guided therapy improved primary outcomes and lowered bleeding rates compared to conventional therapy. However, the benefit varied, with most trials demonstrating improved efficacy and safety outcomes of various statistical significance levels. Meta-analysis of pooled data (n = 3,424) showed a significant reduction in major adverse cardiovascular events (MACE) with point-of-care genotype-guided therapy (RR = 0.56, 95% CI 0.41-0.76, p = 0.0002; moderate heterogeneity I² = 48%) while bleeding outcomes did not differ significantly (RR = 0.74, 95% CI 0.35-1.56, p = 0.42; I² = 51%). PROSPERO registration (CRD42023378028).
This study aimed to assess the prevalence of the use of medications with pharmacogenomic guidelines upon hospital admission in patients aged 65 and over and evaluate its association with adverse outcomes, including length of stay, unplanned admissions, and repeat hospital admissions. A retrospective cross-sectional study was conducted using hospital admissions data from 2018-2019 in one NHS hospital trust in England, focusing on patients aged 65 and over. The usage of medications with pharmacogenomic guidelines was examined, and comparisons were made between their prevalence in unplanned and planned admissions. Multivariable models assessed whether the use of medications with pharmacogenomic guidelines were associated with adverse outcomes, considering frailty status. Analysis of 59,973 admissions revealed 67 pharmacogenomics medicines as per the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, with 11 classified as high-risk among 1438 unique medicines identified from 560,179 recorded medications. Notably, unplanned admissions exhibited a higher prevalence of medications with pharmacogenomic guidelines (84% versus 64%, p < 0.001) compared to planned admissions. The models demonstrated the usage of these medications was associated with adverse outcomes (length of stay in hospital, unplanned admission and repeat hospital admission) with substantial evidence (Delta_AICc < 2) particularly in patients with high frailty status. This study highlights the association between medications with pharmacogenomic guidelines and adverse outcomes, particularly among patients with high frailty. The findings emphasise the importance of integrating pharmacogenomic-guided care into the management of older individuals with frailty to mitigate adverse outcomes and enhance medication safety.
Bladder cancer (BC) is a highly prevalent form of cancer worldwide, and cisplatin (CDDP) resistance poses a major challenge to patients. Cytoplasmic linker-associated protein 2 (CLASP2) is a member of the microtubule plus-end tracking protein family and is involved in the regulation of microtubule dynamics. In this study, we evaluated the influence of CLASP2 on BC progression and cisplatin resistance. Levels of CLASP2, HNRNPA1, NONO, ZRANB2, FUS, KHSRP and QKI in BC tissues and cells were tested by RT-qPCR. Protein levels of CLASP2 and KHSRP were detected by Western blot. Cell viability and IC50 of cisplatin-treated BC cells were measured by CCK-8. Cell proliferation and apoptosis were determined using colony formation assay and flow cytometry, respectively. RNA immunoprecipitation (RIP) and Co-immunoprecipitation (Co-IP) experiments were adopted to verify target genes of CLASP2. Cellular localization of CLASP2 and MAPRE1 was detected utilizing immunofluorescence staining. The xenograft tumor model was established in BALB/c nude mice. We found that iCLASP2 levels were increased in CDDP-resistant BC tissues and cells. Suppression of CLASP2 impeded BC cell proliferation and alleviated their resistance to CDDP. KHSRP positively influenced the stability of CLASP2 mRNA. There was a protein interaction between CLASP2 and MAPRE1. Silencing KHSRP or MAPRE1 reversed the effect exerted of CLASP2 on BC cells. CLASP2 decreased the sensitivity of BC to CDDP in vivo. Our results imply that CLASP2 contributes to tumorigenesis and cisplatin resistance in BC via targeting MAPRE1, thereby promoting BC progression and providing a new therapeutic target for BC treatment.
A survey was conducted to determine attitudes, knowledge, and educational needs of mental health professionals regarding pharmacogenomics. We recruited 128 clinicians working in mental health in England, and we assessed their experiences using an adapted version of the "U-PGx Clinician's Questionnaire". Responding clinicians had positive attitudes towards pharmacogenomics testing, although they lacked confidence in ordering and interpreting tests, for which most had never received any formal training. Only 6% of clinicians answered all 4 knowledge testing questions correctly, and barriers to clinical implementation included lack of familiarity and knowledge for several pharmacogenomics concepts, such as drug metabolism and genetics, as well as needing support from their working institution. Looking ahead, we found that accredited workshops and patient cases were preferred learning formats, and we suggest tailored education programmes to enable mental health professionals to apply pharmacogenomics in clinical practice.
Pharmacogenomic testing for CYP2C19 helps personalise clopidogrel therapy and reduces the risk of experiencing a secondary myocardial infarction in individuals with impaired CYP2C19 function. Routine testing, however, is uncommon and it is proposed that the key requirements and processes of testing services are poorly understood. This scoping review aimed to explore the literature for CYP2C19 testing services for clopidogrel and identify their commonalities to inform the design and delivery of future services. In total, 37 eligible studies describing services across hospital and community settings were retrieved. Key elements of delivery included a multi-disciplinary approach involving physicians and pharmacists, provision of pre-implementation training and education, and electronic communication of test results. Result integration into clinical decision support systems improved the practical application of pharmacogenomic testing. The identification of the key requirements and processes may be used by institutions looking to design and deliver CYP2C19 testing services to guide clopidogrel therapy.
Psoriatic arthritis (PsA) is a complex inflammatory disease characterised by a combination of cutaneous and musculoskeletal symptoms, which complicates both diagnosis and management. Despite advances in targeted therapy, the treatment of PsA remains challenging, with up to 40% of patients experiencing inadequate response. Predictive biomarkers may serve as valuable tools to help clinicians select the most appropriate therapy at the optimal time. However, despite substantial research activity, the current evidence remains inconclusive. Moreover, biomarkers that predict treatment response may differ from those that dynamically change in response to therapy. In this narrative review we therefore examine both biomarkers with potential predictive value and those that demonstrate treatment-related changes over time. Multi-omics approaches have enhanced understanding of the molecular determinants of therapeutic response. Most genetic analyses have investigated TNF inhibitor response, whereas certain transcriptomic studies highlight genes related to apoptosis and immune-mediated pathways as key contributors to therapeutic response across different treatments and throughout the treatment course. Several non-coding RNAs and protein panels have also shown promise as indicators of treatment outcome, but metabolomic studies remain scarce. Reported molecular signatures nonetheless vary widely, likely reflecting disease heterogeneity as well as differences in treatment modalities, study design, and outcome measures, and are summarised herein. Future studies integrating multi-omics and computational frameworks are essential to validate candidate biomarkers and facilitate personalised treatment strategies in PsA.
The Clinical Pharmacogenetics Implementation Consortium (CPIC) has formally updated the SLCO1B1 allele functionality table based on new evidence. Notably, the alleles studied (SLCO1B1 *9, *31, *41) are enriched in the genomes of patients historically excluded from pharmacogenomics research. This perspective illustrates how new evidence can advance clinical pharmacogenetics implementation guidelines incorporation in real time through collaboration with CPIC. Together, we can accelerate the translation of pharmacogenetics into a tool that truly improves health outcomes for everyone.
Proteomics has been scarcely explored for predicting treatment outcomes in major depressive disorder (MDD), due to methodological challenges and costs. Predicting protein levels from genetic scores provides opportunities for exploratory studies and the selection of targeted panels. In this study, we examined the association between genetically predicted plasma proteins and treatment outcomes - including non-response, non-remission, and treatment-resistant depression (TRD) - in 3559 patients with MDD from four clinical samples. Protein levels were predicted from individual-level genotypes using genetic scores from the publicly available OmicsPred database, which estimated genetic scores based on genome-wide genotypes and proteomic measurements from the Olink and SomaScan platforms. Associations between predicted protein levels and treatment outcomes were assessed using logistic regression models, adjusted for potential confounders including population stratification. Results were meta-analysed using a random-effects model. The Bonferroni correction was applied. We analysed 257 proteins for Olink and 1502 for SomaScan; 111 proteins overlapped between the two platforms. Despite no association was significant after multiple-testing correction, many top results were consistent across phenotypes, in particular seven proteins were nominally associated with all the analysed outcomes (CHL1, DUSP13, EVA1C, FCRL2, KITLG, SMAP1, and TIM3/HAVCR2). Additionally, three proteins (CXCL6, IL5RA, and RARRES2) showed consistent nominal associations across both the Olink and SomaScan platforms. The convergence of results across phenotypes is in line with the hypothesis of the involvement of immune-inflammatory mechanisms and neuroplasticity in treatment response. These results can provide hints for guiding the selection of protein panels in future proteomic studies.
This review offers an overview of advanced in silico methods crucial for drug discovery, emphasizing their integration with data science, and investigates the effectiveness of data science, machine learning, and artificial intelligence via a thorough meta-analysis of existing technologies. This meta-analysis aims to rank these technologies based on their applications and accessibility of knowledge. Initially, a search strategy yielded 900 papers, which were then refined into two subsets: the top 300 most-cited papers since 2000 and papers selected for systematic review based on high impact. From these, 97 articles were identified for discussion, categorized by their influence on society. The focus remains on the qualitative impact of these disciplines rather than solely on metrics like new drug approvals. Ultimately, the review underscores the role of big data in enhancing our comprehension of drug candidate trajectories from development to commercialization, utilizing information stored in publicly available databases to chemical space. Graphical extrapolation of some keywords (Drug Discovery; Big Data; Database; Metadata) discussed in this article and their evolution (in terms of absolute items that are available) by time.
Circulating levels of uric acid are influenced by a complex mix of intrinsic and environmental factors, including genetics, diet, and drugs. We analyzed levels of uric acid in a recent phase 3 clinical trial of patients with bipolar mania treated with 24 mg/day of the antipsychotic iloperidone or placebo. Initial results revealed that iloperidone treatment was associated with increases in uric acid from baseline (LS mean change (SE) of 27.2 (-4.93) μmol/L, compared with a change of 0.1 (-4.77) μmol/L for placebo group (LS mean difference (95% CI) = 27.1 (14.94, 39.20), p = <0.0001). Similar results were further observed in a previous phase 3 study of iloperidone treatment of schizophrenia. Pharmacogenetic analysis examining the urate transporter SLC2A9 revealed that iloperidone associated increases were linked to a genetic variant (rs7442295), correlating with both urate levels at baseline and in interaction with iloperidone vs placebo, and a pronounced increase of 35.9 μmol/L (0.67 mg/dL) was seen in iloperidone-treated patients homozygous for the for the rs7442295 (G) allele at the SLC2A9 gene, compared to a decrease of -16.5 (0.31 μmol/L in the corresponding GG placebo group (LS mean difference (95% CI) = 40.79 (14.61, 66.96, p = 0.0024). Further investigation suggested potentially clinically relevant sex differences associated with this variant. Specifically, male GG genotype patients exhibiting more frequent shifts from above the upper limit of normal for iloperidone-treated patients in comparison to female, AG/AA, and placebo groups. Overall, the mechanism of this iloperidone-induced increase in serum urate levels is likely due to decrease in clearance of urate through interaction with the SLC2A9 urate transporter protein. These results may hold clinical significance for patients treated with iloperidone.
This study investigates combine effect of low BMI and possible pharmacogenetic influence of ABC gene polymorphisms in treatment responses of BC patients. BMI was analysed prior to commencement of chemotherapy. Clinical response was evaluated by radiological imaging and categorised as per RECISTv.1 criteria. SNPs (C1236T, C3435T, C58626A) in ABCB1 and ABCC2 gene were selected. 148 patients were analysed using PCR-RFLP. ABCC2 (58626AA) was significantly associated with treatment non-responsiveness in all genetic models namely dominant (OR 2.954; [1.442-6.051]; p = 0.003), recessive (OR 5.723; [2.48-13.20]; p < 0.0001), codominant (χ2 21.219; p < 0.0001). The proportion of ORR and NRs were significantly different between low (<18.5) and high (≥18.5) BMI classes (OR 16.097; [7.12-36.35]; p < 0.0001). Furthermore, when treatment response was combined with BMI groups, significant associations were observed for C58626A SNP across all genetic models among low BMI group: dominant (OR 3.324; [1.012-10.406]; p = 0.041), recessive (OR 7.250; [1.533-34.278]; p = 0.012) and codominant (χ2 8.657; p = 0.013). Both PFS (35.31 months; p = 0.005) and OS (39.75 months; p = 0.032) were lowered among AA genotype (ABCC2) while the hazard risk of this genotype was further increased in low BMI patients (HR 1.963). 3435CT genotypes in ABCB1 gene showed 87% reduction in risk of death (HR 0.13; p = 0.025). Low BMI independently and jointly with 58626AA genotype of ABCC2 gene was responsible for poor chemotherapy response and survival outcome among AC-T regimen receiving BC patients. Together, this study underscores the importance of genetic counselling and nutritional assessment for favourable treatment outcomes.
Pharmacogenetics uses genetic testing to improve the safety and effectiveness of prescribed medicines, yet implementation at scale remains limited due to the absence of interoperable health IT solutions that integrate results into prescribing workflows. This study aimed to develop and validate open data standards for pharmacogenetic results to enable interoperability across healthcare systems. A baseline data model was constructed using the open standard openEHR by synthesising literature, genomic sequencing outputs, and international data specifications, and refined through iterative workshops with the Global Alliance for Genomics and Health. The model underwent two rounds of structured peer review involving 24 experts from 10 countries. Mapping to HL7 FHIR was evaluated using both manual and automated approaches, including the FHIR-Connect tool. The resulting standardised pharmacogenetic data model separates test results from therapeutic implications and incorporates recognised terminologies such as SNOMED CT and HGNC. It achieved international consensus and is published on the openEHR Clinical Knowledge Manager platform. Mapping to HL7 FHIR demonstrated bidirectional information flow within healthcare systems, with automated mapping enabling scalable and reusable transformations. This work provides a framework for storing and exchanging pharmacogenetic test results, supporting semantic harmonisation, interoperability, and integration with clinical decision support systems. Open data standards for pharmacogenetic test results therefore offer a foundation for scalable implementation of pharmacogenetics in routine clinical practice.