Analysis of the current situation of pharmacogenomics in terms of educational and healthcare needs in Egypt and Lebanon.

Pharmacogenomics (PGx) is a practice that investigates the link between genetic differences and drug response in patients. This can improve treatment effectiveness and reduce harmful side effects. However, has yet to be adequately realized in developing nations. Three surveys were conducted between November 2022 to March 2023 in Egypt and Lebanon. The first survey assessed availability of PGx testing in different healthcare facilities; the second one assessed knowledge, interest and attitude toward learning about PGx among pharmacists and physicians; and the third one assessed interest in providing PGx education at academic levels. In Egypt, a few of the surveyed healthcare facilities are conducting some form of pharmacogenetic testing. In Lebanon, very few germline pharmacogenomic tests are offered in Greater Beirut's leading hospitals, and no other testing was recorded. PGx education attracts considerable interest, with 34.3% of pharmacists very interested and 48.8% interested. Similarly, 24.8% of total physicians were very interested while 44.8% were interested. Academic professionals in the surveyed institutions in both countries agreed on the need for educational programs in PGx and 78.2% agreed that there were good opportunities for implementing PGx testing. These findings clearly indicate the need to develop and implement educational programs in PGx in the Middle-East.

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Machine learning in oncological pharmacogenomics: advancing personalized chemotherapy.

This review analyzes the application of machine learning (ML) in oncological pharmacogenomics, focusing on customizing chemotherapy treatments. It explores how ML can analyze extensive genomic, proteomic, and other omics datasets to identify genetic patterns associated with drug responses. This, in turn, facilitates personalized therapies that are more effective and have fewer side effects. Recent studies have emphasized ML's revolutionary role of ML in personalized oncology treatment by identifying genetic variability and understanding cancer pharmacodynamics. Integrating ML with electronic health records and clinical data shows promise in refining chemotherapy recommendations by considering the complex influencing factors. Although standard chemotherapy depends on population-based doses and treatment regimens, customized techniques use genetic information to tailor treatments for specific patients, potentially enhancing efficacy and reducing adverse effects.However, challenges, such as model interpretability, data quality, transparency, ethical issues related to data privacy, and health disparities, remain. Machine learning has been used to transform oncological pharmacogenomics by enabling personalized chemotherapy treatments. This review highlights ML's potential of ML to enhance treatment effectiveness and minimize side effects through detailed genetic analysis. It also addresses ongoing challenges including improved model interpretability, data quality, and ethical considerations. The review concludes by emphasizing the importance of rigorous clinical trials and interdisciplinary collaboration in the ethical implementation of ML-driven personalized medicine, paving the way for improved outcomes in cancer patients and marking a new frontier in cancer treatment.

Clinical Application of Therapeutic Drug Monitoring and Pharmacogenetics in Psychiatry, with a Focus on Belgium.

Response rate to treatment is generally not as high as expected in psychiatric disorders. The lack of clinical improvement under a well-conducted treatment, that complies with guidelines, may be the consequence of genetic abnormalities that impact the metabolizing pathways of the drug. Genetic polymorphism of metabolizing enzymes is frequent in the population and has been proven to have a clinical impact. It may also be the consequence of environmental or organic factors that interact with the pharmacokinetic pathways (absorption, distribution, metabolizing, excretion) of the drug. These intrinsic and extrinsic factors will lead to inter- and intraindividual fluctuations in plasma drug concentrations. Therapeutic drug monitoring permits to measure plasma drug concentrations in order to adapt psychopharmacotherapy individually. In some cases, it can be coupled to pharmacogenetic testings. This review presents recent literature and guidelines on the subject. Eventually, there is a focus made on the French-speaking part of Belgium where neither therapeutic drug monitoring, nor pharmacogenetics testing, are used frequently in clinical practice. Some challenges are to be addressed to implement these techniques in Belgium.

The influence of age, gender and pharmacogenetic profiles on the perspective on medicines in the German EMPAR study.

BACKGROUND: Pharmacogenetic testing in routine care could provide benefits for patients, doctors and statutory health insurances. Therefore, the aim of the retrospective, observational study Einfluss metabolischer Profile auf die Arzneimitteltherapiesicherheit in der Routineversorgung (EMPAR) was to analyze the relationship between pharmacogenetic profiles, the risk of adverse drug reactions, and patients' perceptions of drug therapy in 10748 adult (≥18 years) participants in Germany. METHODS: A questionnaire was used to assess views and beliefs about medicines and participants individual perception of sensitivity to drug therapies. The questionnaire consisted of the Beliefs about Medicines Questionnaire (BMQ)-General scales (Overuse, Harm, Benefit), the Perceived Sensitivity to Medicines (PSM), Natural Remedy, and Gene Testing scales. The influence of gender, age, study collective, genotype and phenotype of relevant pharmacogenes on participant's perception were evaluated. RESULTS: Overuse, PSM and Benefit scores were significantly higher among patients of the collective International Classification of Diseases and Health Related Disorders (ICD)-10 Y57.9! diagnosis, which indicates complications related to drugs, compared to the anticoagulant/antiplatelet and cholesterol-lowering drug collective. Age and gender also played a significant role in patients' perceptions, with younger patients and female participants more likely to believe in medication overuse according to the Overuse scale score compared to older and male participants. Female participants compared to male participants and the old age group compared to the young and/or middle-age subgroup, scored higher in PSM and/or Harm scales, respectively. Only a tendency of increased Harm, Overuse and PSM scores was observed in the participant group with five or more relevant actionable variants compared to subgroups with 0 up to 4 variants. CONCLUSIONS: In conclusion, patients' beliefs about medicines and their drug sensitivity perceptions are influenced by various factors including age, gender, previous complications with medicines, and with some tendency also pharmacogenetic profiles. The higher association with more negative views related to treatment indicates that there is a need to target the underlying issues in affected patient groups in order to improve compliance to treatment and outcomes in routine care. Trial registration: EMPAR was registered in the German Clinical Trials Register (DRKS) on 06 July 2018 (DRKS00013909).

Clinical and economic outcomes of a pharmacogenomics-enriched comprehensive medication management program in a self-insured employee population.

Clinical and economic outcomes from a pharmacogenomics-enriched comprehensive medication management program were evaluated over 26 months in a self-insured U.S. employee population (n = 452 participants; n = 1500 controls) using propensity matched pre-post design with adjusted negative binomial and linear regression models. After adjusting for baseline covariates, program participation was associated with 39% fewer inpatient (p = 0.05) and 39% fewer emergency department (p = 0.002) visits, and with 21% more outpatient visits (p < 0.001) in the follow-up period compared to the control group. Results show pharmacogenomics-enriched comprehensive medication management can favorably impact healthcare utilization in a self-insured employer population by reducing emergency department and inpatient visits and can offer the potential for cost savings. Self-insured employers may consider implementing pharmacogenomics-enriched comprehensive medication management to improve the healthcare of their employees.

A Systematic Review Exploring Empirical Pharmacogenomics Research Within Global Indigenous Populations.

BACKGROUND: This systematic review aims to highlight the scope of pharmacogenomics research within global Indigenous populations. This review also explores the barriers and facilitators of pharmacogenomics research within this population. METHODOLOGY: A systematic review of literature was conducted to identify and present an understanding of current empirical evidence demonstrating the conduct of genomics or pharmacogenomics research within global Indigenous populations (PROSPERO registration: CRD42021257226). Using key search terms, relevant databases were searched for articles published between January 2010 and July 2022. Screening, data extraction, and analysis was conducted using well-defined inclusion criteria. Quality assessment and risk of bias appraisal was conducted using the mixed methods appraisal tool. Indigenous community engagement and participation in genomics research was assessed using the social-ecological framework. RESULTS: From the 427 articles identified, 77 articles met inclusion criteria and underwent full-text screening. Of these, 30 articles were included in the final review, with 16 being quantitative and 14 either qualitative or mixed methods studies. Most studies were conducted with native Indigenous populations from the United States of America (36%). Content analysis revealed that studies either explored genetic variations associated with disease in Indigenous populations (23%) or markers for drug metabolism (30%) or were designed to understand perspectives of genomics research within this population (47%). Perspectives included the exploration of the role of participants in research, benefits or outcomes achieved from participation in genomics research, and levels of Indigenous engagement and participation in genomics research. CONCLUSIONS: This review highlights a growing gap in Indigenous genomics research globally. It presents several important considerations from Indigenous participants, identifying how researchers can co-create culturally safe and inclusive design, implementation, analysis, and subsequent outcomes of genomics research involving Indigenous people. Indigenous governance, self-determination and leadership is essential, with researchers required to be responsive to such fundamental partnerships for research to progress.

[Selection of pharmacogenomic variants and methodology for their use in community pharmacy]. / Selección de variantes farmacogenómicas y metodología para su uso en farmacia comunitaria.

Regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) recognize pharmacogenetics as a key tool in their pharmacological guidelines for pharmaceutical counseling. In this context, community pharmacies play a crucial role in addressing this healthcare need, which could lead to a significant improvement in patients' quality of life by preventing ineffective or contraindicated treatments.In this work, we conducted a systematic review of the available scientific evidence regarding druggene interactions relevant to community pharmacy. We identified the main genes and polymorphisms associated with treatment response and adverse effects in primary care. Finally, we propose a model for implementing pharmacogenetic services in community pharmacies.

Pharmacogenetic analysis of structural variation in the 1000 genomes project using whole genome sequences.

While significant strides have been made in understanding pharmacogenetics (PGx) and gene-drug interactions, there remains limited characterization of population-level PGx variation. This study aims to comprehensively profile global star alleles (haplotype patterns) and phenotype frequencies in 58 pharmacogenes associated with drug absorption, distribution, metabolism, and excretion. PyPGx, a star-allele calling tool, was employed to identify star alleles within high-coverage whole genome sequencing (WGS) data from the 1000 Genomes Project (N = 2504; 26 global populations). This process involved detecting structural variants (SVs), such as gene deletions, duplications, hybrids, as well as single nucleotide variants and insertion-deletion variants. The majority of our PyPGx calls for star alleles and phenotype frequencies aligned with the Pharmacogenomics Knowledge Base, although notable population-specific frequencies differed at least twofold. Validation efforts confirmed known SVs while uncovering several novel SVs currently undefined as star alleles. Additionally, we identified 210 small nucleotide variants associated with severe functional consequences that are not defined as star alleles. The study serves as a valuable resource, providing updated population-level star allele and phenotype frequencies while incorporating SVs. It also highlights the burgeoning potential of cost-effective WGS for PGx genotyping, offering invaluable insights to improve tailored drug therapies across diverse populations.

Pharmacogenetics of DPYD and treatment-related mortality on fluoropyrimidine chemotherapy for cancer patients: a meta-analysis and trial sequential analysis.

BACKGROUND: Fluoropyrimidines are chemotherapy drugs utilized to treat a variety of solid tumors. These drugs predominantly rely on the enzyme dihydropyrimidine dehydrogenase (DPD), which is encoded by the DPYD gene, for their metabolism. Genetic mutations affecting this gene can cause DPYD deficiency, disrupting pyrimidine metabolism and increasing the risk of toxicity in cancer patients treated with 5-fluorouracil. The severity and type of toxic reactions are influenced by genetic and demographic factors and, in certain instances, can result in patient mortality. Among the more than 50 identified variants of DPYD, only a subset has clinical significance, leading to the production of enzymes that are either non-functional or impaired. The study aims to examine treatment-related mortality in cancer patients undergoing fluoropyrimidine chemotherapy, comparing those with and without DPD deficiency. METHODS: The meta-analysis selected and evaluated 9685 studies from Pubmed, Cochrane, Embase and Web of Science databases. Only studies examining the main DPYD variants (DPYD*2A, DPYD p.D949V, DPYD*13 and DPYD HapB3) were included. Statistical Analysis was performed using R, version 4.2.3. Data were examined using the Mantel-Haenszel method and 95% CIs. Heterogeneity was assessed with I2 statistics. RESULTS: There were 36 prospective and retrospective studies included, accounting for 16,005 patients. Most studies assessed colorectal cancer, representing 86.49% of patients. Other gastrointestinal cancers were evaluated by 11 studies, breast cancer by nine studies and head and neck cancers by five studies. Four DPYD variants were identified as predictors of severe fluoropyrimidines toxicity in literature review: DPYD*2A (rs3918290), DPYD p.D949V (rs67376798), DPYD*13 (rs55886062) and DPYD Hap23 (rs56038477). All 36 studies assessed the DPYD*2A variant, while 20 assessed DPYD p.D949V, 7 assessed DPYD*13, and 9 assessed DPYDHap23. Among the 587 patients who tested positive for at least one DPYD variant, 13 died from fluoropyrimidine toxicity. Conversely, in the non-carrier group there were 14 treatment-related deaths. Carriers of DPYD variants was found to be significantly correlated with treatment-related mortality (OR = 34.86, 95% CI 13.96-87.05; p < 0.05). CONCLUSIONS: This study improves our comprehension of how the DPYD gene impacts cancer patients receiving fluoropyrimidine chemotherapy. Identifying mutations associated with dihydropyrimidine dehydrogenase deficiency may help predict the likelihood of serious side effects and fatalities. This knowledge can be applied to adjust medication doses before starting treatment, thus reducing the occurrence of these critical outcomes.

Comparative performance of pharmacogenetics-based warfarin dosing algorithms in Chinese population: use of a pharmacokinetic/pharmacodynamic model to explore dosing regimen through clinical trial simulation.

OBJECTIVE: Warfarin has a narrow therapeutic window and large variability in dosing that are affected by clinical and genetic factors. To help guide the dosing of warfarin, the Clinical Pharmacogenetics Implementation Consortium has recommended the use of pharmacogenetic algorithms, such as the ones developed by the International Warfarin Pharmacogenetics Consortium (IWPC) and by Gage et al. when genotype information is available. METHODS: In this study, simulations were performed in Chinese cohorts to explore how dosing differences between Western (by IWPC and Gage et al.) and Chinese algorithms (by Miao et al.) would mean in terms of anticoagulation effect in clinical trials. We first tried to replicate a published clinical trial comparing genotype-guided dosing to routine clinical dosing in Chinese patients. We then made simulations where Chinese cohorts received daily doses recommended by Gage, IWPC, and Miao algorithms. RESULTS: We found that in simulation conditions where dosing specifications were strictly followed, genotype-guided dosing by IWPC and Lenzini formulae was more likely to overshoot the upper limit of the therapeutic window by day 15, and thus may have a lower % time in therapeutic range (%TTR) than that of clinical dosing group. Also, in comparing Gage, IWPC, and Miao algorithms, we found that the Miao dosing cohort has the highest %TTR and the lowest risk of over-anticoagulation by day 28. CONCLUSION: In summary, our results confirmed that algorithms developed based on data from local patients may be more suitable for achieving therapeutic international normalized ratio window in Chinese population.

Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.

Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient-specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health-system-wide, mixed-methods study. Clinicians representing multiple practices and care settings (N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient-specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0-100 points) of each alert, the perceived impact of each alert on stress and decision-making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision-making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician-guided design of PGx alerts in the era of digital medicine.

Pharmacogenomics-assisted treatment versus standard of care in schizophrenia: a systematic review and meta-analysis.

BACKGROUND: Pharmacogenomic (PGx) factors significantly influence how patients respond to antipsychotic medications This systematic review was performed to synthesize the clinical utility of PGx-assisted treatment versus standard of care in schizophrenia. METHODS: PubMed, Embase, and Cochrane CENTRAL databases were searched for randomized controlled trials (RCTs) from inception till June 2024 that had compared the clinical utility of PGx-assisted intervention as compared to the standard of care in schizophrenia. The primary outcome was safety, and the secondary outcomes were efficacy and medication adherence. Pooled standardized mean differences (SMD) along with a 95% confidence interval (CI) were calculated (random-effects model) wherever feasible. RESULTS: A total of 18,821 studies were screened, and five were included for review. All the RCTs had a high risk of bias. Four studies included the commonly used antipsychotics. Three studies reported negative outcomes (safety, efficacy, and medication adherence) and two reported positive outcomes (safety) using different scales. In the meta-analysis, there were significant differences in the total Udvalg for Kliniske Undersogelser Side-Effect Rating scale score [SMD 0.95 (95% CI: 0.76-1.13), p < 0.001); I2 = 0%] and the total Positive and Negative Syndrome Scale score [SMD 10.65 (95% CI: 2.37-18.93), p = 0.01); I2 = 100%] between the PGx-assisted treatment and standard of care arms. However, the results were inconsistent, and the certainty of evidence (GRADE criteria) was very low. CONCLUSION: Current evidence on the clinical utility of PGx-assisted treatment in schizophrenia is limited and inconsistent and further evidence is required in this regard.

Drug-gene interactions in older patients with coronary artery disease.

BACKGROUND: Older patients with coronary artery disease (CAD) are particularly vulnerable to the efficacy and adverse drug reactions, and may therefore particularly benefit from personalized medication. Drug-gene interactions (DGIs) occur when an individual's genotype affects the pharmacokinetics and/or pharmacodynamics of a victim drug. OBJECTIVES: This study aimed to investigate the impact of cardiovascular-related DGIs on the clinical efficacy and safety outcomes in older patients with CAD. METHODS: Hospitalized older patients (≥ 65 years old) with CAD were consecutively recruited from August 2018 to May 2022. Eligible patients were genotyped for the actionable pharmacogenetic variants of CYP2C9, CYP2C19, CYP2D6, CYP3A5, and SLCO1B1, which had clinical annotations or implementation guidelines for cardiovascular drugs. Allele frequencies and DGIs were determined in the cohort for the 5 actionable PGx genes and the prescribed cardiovascular drugs. All patients were followed up for at least 1 year. The influence of DGIs on the cardiovascular drug-related efficacy outcomes (all-cause mortality and/or major cardiovascular events, MACEs) and drug response phenotypes of "drug-stop" and "dose-decrease" were evaluated. RESULTS: A total of 1,017 eligible older patients with CAD were included, among whom 63.2% were male, with an average age of 80.8 years old, and 87.6% were administrated with polypharmacy (≥ 5 medications). After genotyping, we found that 96.0% of the older patients with CAD patients had at least one allele of the 5 pharmacogenes associated with a therapeutic change, indicating a need for a therapeutic change in a mean of 1.32 drugs of the 19 cardiovascular-related drugs. We also identified that 79.5% of the patients had at least one DGI (range 0-6). The median follow-up interval was 39 months. Independent of age, negative association could be found between the number of DGIs and all-cause mortality (adjusted HR: 0.84, 95% CI: 0.73-0.96, P = 0.008), and MACEs (adjusted HR: 0.84, 95% CI: 0.72-0.98, P = 0.023), but positive association could be found between the number of DGIs and drug response phenotypes (adjusted OR: 1.24, 95% CI: 1.05-1.45, P = 0.011) in the elderly patients with CAD. CONCLUSIONS: The association between cardiovascular DGIs and the clinical outcomes emphasized the necessity for the integration of genetic and clinical data to enhance the optimization of cardiovascular polypharmacy in older patients with CAD. The causal relationship between DGIs and the clinical outcomes should be established in the large scale prospectively designed cohort study.

An Inexpensive and Quick Method for Genotyping of HLA Variants Included in the Spanish Pharmacogenomic Portfolio of National Health System.

The possibility of using the same genotyping technology (TaqMan) for all the genetic tests included in the new Spanish pharmacogenomics portfolio should enable the application of a multigenotyping platform to obtain a whole pharmacogenomics profile. However, HLA-typing is usually performed with other technologies and needs to be adapted to TaqMan assays. Our aim was to establish a set of TaqMan assays for correct typing of HLA-A*31:01, HLA-B*15:02, HLA-B*57:01, and HLA-B*58:01. Therefore, we searched for and selected SNVs described in different populations as surrogate markers for these HLA alleles, designed TaqMan assays, and tested in a set of samples with known HLA-A and HLA-B. HLA-A*31:01 was correctly typed with a combination of rs1061235 and rs17179220 (PPV 100%, 95% CI 84.6-100-%; NPV 100%, 95% CI 96.5-100.0%), HLA-B*15:02 with rs10484555 (PPV 100%, 95% CI 69.2-100.0%; NPV 100%, 95% CI 96.8-100.0%) and rs144012689 (PPV 100%, 95% CI 69.2-100.0%; NPV 100%, 95% CI 96.8-100.0%), and HLA-B*57:01 with rs2395029 (PPV 99.5%, 95% CI 72.9-99.3%; NPV 99.5%, 95% CI 98.3-100.0%). HLA-B*58:01 was typed using two allele-specific TaqMan probes mixed with a ß-Globin reference and treated as a genotyping assay (PPV 100.0%, 95% CI 81.5-100.0%; NPV 100%, 95% CI 96.8-100.0%). In conclusion, we demonstrated a clinically useful way to type HLA-A and HLA-B alleles included in the Spanish pharmacogenomics portfolio using TaqMan assays.

Thai pharmacogenomics database -2 (TPGxD-2) sequel to TPGxD-1, analyzing genetic variants in 26 non-VIPGx genes within the Thai population.

Next-generation sequencing (NGS) has transformed pharmacogenomics (PGx), enabling thorough profiling of pharmacogenes using computational methods and advancing personalized medicine. The Thai Pharmacogenomic Database-2 (TPGxD-2) analyzed 948 whole genome sequences, primarily from the Electricity Generating Authority of Thailand (EGAT) cohort. This study is an extension of the previous Thai Pharmacogenomic Database (TPGxD-1) and specifically focused on 26 non-very important pharmacogenes (VIPGx) genes. Variant calling was conducted using Sentieon (version 201808.08) following GATK's best workflow practices. We then annotated variant call format (VCF) files using Golden Helix VarSeq 2.5.0. Star allele analysis was performed with Stargazer v2.0.2, which called star alleles for 22 of 26 non-VIPGx genes. The variant analysis revealed a total of 14,529 variants in 26 non-VIPGx genes, with TBXAS1 had the highest number of variants (27%). Among the 14,529 variants, 2328 were novel (without rsID), with 87 identified as clinically relevant. We also found 56 known PGx variants among the known variants (n = 12,201), with UGT2B7 (19.64%), CYP1B1 (8.9%), SLCO2B1 (8.9%), and POR (8.9%) being the most common. We reported a high frequency of intermediate metabolizers (IMs) in CYP2F1 (34.6%) and CYP4A11 (8.6%), and a high frequency of decreased functional alleles in POR (53.9%) and SLCO1B3 (34.9%) genes. This study enhances our understanding of pharmacogenomic profiling of 26 non-VIPGx genes of notable clinical importance in the Thai population. However, further validation with additional computational and reference genotyping methods is necessary, and novel alleles identified in this study should undergo further orthogonal validation.

Evaluating AlphaFold for Clinical Pharmacology and Pharmacogenetics: A Case-Study of Huntingtin Variants Linked to Huntington's Disease.

To evaluate the artificial intelligence (AI)-guided AlphaFold algorithm for studying the binding interactions of human huntingtin and the aggregation of huntingtin peptides. Variants of huntingtin protein implicated in Huntington's disease were used as a model system to evaluate AlphaFold. Variants of huntingtin and huntingtin peptides with polyglutamine tracts (PQT) containing 21, 31, 51, or 78 glutamines were studied. The 3-dimensional structures of huntingtin variants and their interactions with huntingtin-associated protein-40 (HAP40) were obtained. Aggregation experiments were conducted with peptide sequences corresponding to variants of PQT, amino terminal sequence (NTS) plus PQT, NTS plus PQT plus proline rich region (PRR), and the 300 amino acid sequence from the NTS through HEAT3 of huntingtin. Oligomerization experiments with 1, 3, 6, or 12 peptide sequences were used to assess the quaternary structures of aggregates. The PQT and PQT plus NTS peptides formed a helical secondary structure that formed a central core in the quaternary structure of the aggregates The PRR formed an extended type II polyproline helix that did not participate in central core the aggregates. The distance between the amino and carboxyl termini of disease-linked 31Q, 51Q, and 78Q variants of full-length huntingtin was prominently decreased compared to the 21Q huntingtin. The interaction of HAP40 with the 78Q variant increased the distance between the amino and carboxyl termini. AlphaFold identified key tertiary structure changes in human huntingtin that have been independently corroborated in experimental models. The results highlight the utility of AlphaFold for hypothesis generation in pharmaceutical research.

Genotype-Guided Antiplatelet Therapy: JACC Review Topic of the Week.

The clinical efficacy and safety of antiplatelet agents vary among patients. Consequently, some patients are at increased risk of recurrent ischemic events during treatment. This interindividual variability can be a result of genetic variants in enzymes that play a role in drug metabolism. The field of pharmacogenomics explores the influence of these genetic variants on an individual's drug response. Tailoring antiplatelet treatment based on genetic variants can potentially result in optimized dosing or a change in drug selection. Most evidence supports guiding therapy based on the CYP2C19 allelic variants in patients with an indication for dual antiplatelet therapy. In ticagrelor-treated or prasugrel-treated patients, a genotype-guided de-escalation strategy can reduce bleeding risk, whereas in patients treated with clopidogrel, an escalation strategy may prevent ischemic events. Although the clinical results are promising, few hospitals have implemented these strategies. New results, technological advancements, and growing experience may potentially overcome current barriers for implementation in the future.