Pharmacogenomics: the genetics of variable drug responses

Rationale and design for a pragmatic randomized trial to assess gene-based prescribing for SSRIs in the treatment of depression

Specific selective serotonin reuptake inhibitors (SSRIs) metabolism is strongly influenced by two pharmacogenes, CYP2D6 and CYP2C19. However, the effectiveness of prospectively using pharmacogenetic variants to select or dose SSRIs for depression is uncertain in routine clinical practice. The objective of this prospective, multicenter, pragmatic randomized controlled trial is to determine the effectiveness of genotype-guided selection and dosing of antidepressants on control of depression in participants who are 8 years or older with ≥3 months of depressive symptoms who require new or revised therapy. Those randomized to the intervention arm undergo pharmacogenetic testing at baseline and receive a pharmacy consult and/or automated clinical decision support intervention based on an actionable phenotype, while those randomized to the control arm have pharmacogenetic testing at the end of 6-months. In both groups, depression and drug tolerability outcomes are assessed at baseline, 1 month, 3 months (primary), and 6 months. The primary end point is defined by change in Patient-Reported Outcomes Measurement Information System (PROMIS) Depression score assessed at 3 months versus baseline. Secondary end points include change inpatient health questionnaire (PHQ-8) measure of depression severity, remission rates defined by PROMIS score < 16, medication adherence, and medication side effects. The primary analysis will compare the PROMIS score difference between trial arms among those with an actionable CYP2D6 or CYP2C19 genetic result or a CYP2D6 drug-drug interaction. The trial has completed accrual of 1461 participants, of which 562 were found to have an actionable phenotype to date, and follow-up will be complete in April of 2024.

Proportion of Antipsychotics with CYP2D6 Pharmacogenetic (PGx) Associations Prescribed in an Early Intervention in Psychosis (EIP) Cohort: A Cross-Sectional Study

BACKGROUND

Prescribing drugs for psychosis (antipsychotics) is challenging due to high rates of poor treatment outcomes, which are in part explained by an individual's genetics. Pharmacogenomic (PGx) testing can help clinicians tailor the choice or dose of psychosis drugs to an individual's genetics, particularly psychosis drugs with known variable response due to CYP2D6 gene variants ('CYP2D6-PGx antipsychotics').

AIMS

This study aims to investigate differences between demographic groups prescribed 'CYP2D6-PGx antipsychotics' and estimate the proportion of patients eligible for PGx testing based on current pharmacogenomics guidance.

METHODS

A cross-sectional study took place extracting data from 243 patients' medical records to explore psychosis drug prescribing, including drug transitions. Demographic data such as age, sex, ethnicity, and clinical sub-team were collected and summarised. Descriptive statistics explored the proportion of 'CYP2D6-PGx antipsychotic' prescribing and the nature of transitions. We used logistic regression analysis to investigate associations between demographic variables and prescription of 'CYP2D6-PGx antipsychotic' versus 'non-CYP2D6-PGx antipsychotic'.

RESULTS

Two-thirds (164) of patients had been prescribed a 'CYP2D6-PGx antipsychotic' (aripiprazole, risperidone, haloperidol or zuclopenthixol). Over a fifth (23%) of patients would have met the suggested criteria for PGx testing, following two psychosis drug trials. There were no statistically significant differences between age, sex, or ethnicity in the likelihood of being prescribed a 'CYP2D6-PGx antipsychotic'.

CONCLUSIONS

This study demonstrated high rates of prescribing 'CYP2D6-PGx-antipsychotics' in an EIP cohort, providing a rationale for further exploration of how PGx testing can be implemented in EIP services to personalise the prescribing of drugs for psychosis.

Multi-lineage heart-chip models drug cardiotoxicity and enhances maturation of human stem cell-derived cardiovascular cells

Cardiovascular toxicity causes adverse drug reactions and may lead to drug removal from the pharmaceutical market. Cancer therapies can induce life-threatening cardiovascular side effects such as arrhythmias, muscle cell death, or vascular dysfunction. New technologies have enabled cardiotoxic compounds to be identified earlier in drug development. Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (CMs) and vascular endothelial cells (ECs) can screen for drug-induced alterations in cardiovascular cell function and survival. However, most existing hiPSC models for cardiovascular drug toxicity utilize two-dimensional, immature cells grown in static culture. Improved in vitro models to mechanistically interrogate cardiotoxicity would utilize more adult-like, mature hiPSC-derived cells in an integrated system whereby toxic drugs and protective agents can flow between hiPSC-ECs that represent systemic vasculature and hiPSC-CMs that represent heart muscle (myocardium). Such models would be useful for testing the multi-lineage cardiotoxicities of chemotherapeutic drugs such as VEGFR2/PDGFR-inhibiting tyrosine kinase inhibitors (VPTKIs). Here, we develop a multi-lineage, fully-integrated, cardiovascular organ-chip that can enhance hiPSC-EC and hiPSC-CM functional and genetic maturity, model endothelial barrier permeability, and demonstrate long-term functional stability. This microfluidic organ-chip harbors hiPSC-CMs and hiPSC-ECs on separate channels that can be subjected to active fluid flow and rhythmic biomechanical stretch. We demonstrate the utility of this cardiovascular organ-chip as a predictive platform for evaluating multi-lineage VPTKI toxicity. This study may lead to the development of new modalities for the evaluation and prevention of cancer therapy-induced cardiotoxicity.

Pharmacokinetics of oral ciprofloxacin in adult patients: A scoping review

AIMS

To map the literature on oral ciprofloxacin's pharmacokinetics and its implications for dose adjustments in specific populations.

METHODS

A scoping review was performed according to the Cochrane Collaboration and JBI and reported following the PRISMA-ScR. Systematic searches on electronic databases were conducted to integrate the current evidence on ciprofloxacin's pharmacokinetics. The quality of the included studies was assessed using ClinPK's checklist.

RESULTS

The search yielded 55 relevant studies. Within the traditional pharmacokinetics studies (n = 46), 86 profiles were examined (72 involving healthy patients and 14 with various clinical conditions). Oral ciprofloxacin's pharmacokinetics were influenced by covariates such as drug interactions (ferrous ions, calcium carbonate, diclofenac and itraconazole), food interactions (calcium-rich foods), elderly populations and renal impairment. Notably, variability in pharmacokinetic parameters existed among subjects, regardless of their health status, underscoring the need for comprehensive population descriptions. Population pharmacokinetic studies (n = 9) identified significant covariates for hospitalized patients, such as creatinine clearance, plasma bicarbonate, estimated glomerular filtration rate, renal replacement therapy, age, sex, total bilirubin, fat-free mass, dietary factors in renal disease, rifampicin for clearance models and body weight for volume of distribution models. Most pharmacokinetic/pharmacodynamic assessments concluded that 1200 mg/day provides a high probability of target attainment for bacteria with minimum inhibitory concentration <0.5 mg L-1 , aiming for an area under the curve for 24 h/minimum inhibitory concentration >125 h.

CONCLUSIONS

This study offers a comprehensive overview regarding oral ciprofloxacin's pharmacokinetics across various health conditions. It highlights the complexities of ciprofloxacin's pharmacokinetics, emphasizing the importance of considering multiple factors in dose adjustments.

Relevance of pharmacogenetics and pharmacogenomics in veterinary clinical practice: A review

The recent advances in high-throughput next-generation sequencing technologies have heralded the arrival of the Big Data era. As a result, the use of pharmacogenetics in drug discovery and individualized drug therapy has transformed the field of precision medicine. This paradigm shift in drug development programs has effectively reshaped the old drug development practices, which were primarily concerned with the physiological status of patients for drug development. Pharmacogenomics bridges the gap between pharmacodynamics and pharmacokinetics, advancing current diagnostic and treatment strategies and enabling personalized and targeted drug therapy. The primary goals of pharmacogenetic studies are to improve drug efficacy and minimize toxicities, to identify novel drug targets, to estimate drug dosage for personalized medicine, and to incorporate it as a routine diagnostic for disease susceptibility. Although pharmacogenetics has numerous applications in individualized drug therapy and drug development, it is in its infancy in veterinary medicine. The objective of this review is to present an overview of historical landmarks, current developments in various animal species, challenges and future perspectives of genomics in drug development and dosage optimization for individualized medicine in veterinary subjects.

Managing Diabetic Complications with Alternative Therapeutic Strategies

Diabetes is a chronic metabolic disease affecting millions worldwide. It is characterized by a lack of insulin production or impaired insulin function, leading to elevated blood glucose levels. Conventional treatment methods for diabetes management typically include lifestyle changes and medications. However, alternative therapies have gained attention in recent years, including traditional medicine containing bioactive compounds, supplements like vitamin D and Omega-3 fatty acids, aromatherapy, and homeopathy. Diabetic complications are common in patients with uncontrolled diabetes and can lead to serious health problems, including diabetic retinopathy, impaired wound healing, kidney disease, nerve damage, and cardiovascular disease. Alternative remedies, such as traditional medicine containing bioactive compounds, supplements, and aromatherapy, have been studied for their potential benefits in managing these complications. Traditional medicines like bitter melon, cinnamon, and fenugreek have been shown to have anti-diabetic effects due to their bioactive compounds. Similarly, supplements like vitamin D and Omega-3 fatty acids have been found to improve glycemic control in patients with diabetes. Aromatherapy, which involves the use of essential oils, has also been explored for its potential benefits in diabetes management. Homeopathy, which uses highly diluted substances to stimulate the body's natural healing abilities, has been used to treat diabetes-related symptoms like neuropathy and wounds. Personalized care is essential in natural diabetes management because each person's body and health needs are unique. A holistic approach that addresses the individual's physical, emotional, and spiritual well-being is essential. As research in this field continues to expand, a more comprehensive understanding of diabetes management will lead to improved outcomes for those living with this condition.

Pharmacokinetics and bioequivalence of Withania somnifera (Ashwagandha) extracts - A double blind, crossover study in healthy adults

Introduction

Withania somnifera (WS) or ashwagandha is an adaptogenic plant used extensively in traditional medicines and as a food supplement. Despite a long history of use and numerous clinical trials, the human pharmacokinetics of withanolides, the active phytochemicals in WS extracts, have not been fully evaluated. This study evaluated the oral pharmacokinetics and bioequivalence of active withanolides in human plasma after administration of a single dose of two commercial ashwagandha extracts containing equal amounts of total withanolides.

Methods

This randomized, double-blind, single-dose crossover study of 16 healthy human volunteers evaluated the acute oral bioavailability of withanolides and the bioequivalence of two WS extracts, WS-35 and WS-2.5. WS-35 was standardized to total withanolides not less than 40% comprising not less than 35% withanolide glycosides and WS-2.5 was standardized to 2.5% withanolides. The clinical dosages were normalized to 185 mg of total withanolide in each extract at the bioequivalent dosages. The pharmacokinetic parameters of withanolide A, withanoside IV, withaferin A, and total withanolides were quantified in the blood plasma using a validated LC-MS/MS method.

Results

The half-life, C-max, and mean residence time of the total withanolides were 5.18, 5.62 and 4.13 times significantly higher and had lower systemic clearance with WS-35 than with WS-2.5 extract. Considering the plasma AUC 0-inf of total withanolides per mg of each WS extract administered orally, WS-35 was 280.74 times more bioavailable than WS-2.5.

Conclusion

The results of this study highlight the importance of withanolide glycosides in improving the pharmacokinetics of WS extracts. Owing to its superior pharmacokinetic profile, WS-35, with 35% withanolide glycosides, is a promising candidate for further studies on Withania somnifera.

Clinical trial registration

CTRI/2020/10/028397 [registered on:13/10/2020] (Trial prospectively registered) http://ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=42149&EncHid=&userName=CTRI/2020/10/028397.

Lack of exposure to pharmacogenomics education among the health care providing students in the West Bank of Palestine

OBJECTIVES

Evaluating the knowledge in pharmacogenomics (PGx) is the first step toward the implementation of PGx testing in clinical practice. This survey aimed to evaluate the knowledge of PGx testing among healthcare providing students at the top-ranked university in the West Bank of Palestine.

METHODS

First an online questionnaire consisting of 30 questions regarding the demographic, knowledge, and attitude toward pharmacogenomics testing was structured and validated. Then the questionnaire was distributed to 1,000 current students from different fields.

RESULTS

696 responses was received. The results showed that almost half of the participants (n=355, 51.1%) have never took any courses about PGx during their university training. Only 81 (11.7%) of the students who took the PGx course stated that it helped them understanding how genetic variations affect drug response. The majority of the students were uncertain (n=352, 50.6%) or disagreed (n=143, 20.6%) that the lectures during university education described the effects of genetic variants on drug response. Although most of the students (70-80%) answered that genetic variants can indeed affect the drug's response, only 162 students (23.3%) responded that VKORC1 and CYP2C9 genotypes influence the response to warfarin. In addition, only 94 (13.5%) students were aware that many medicine labels include clinical information about PGx testing provided by the FDA.

CONCLUSIONS

It is concluded from the results of this survey that there is a lack of exposure to PGx education associated with poor knowledge of PGx testing among the healthcare providing students in the West Bank of Palestine. It is recommended to include and improve the lectures and courses regarding PGx as this will have a major impact on precision medicine.

Identification of Novel Intronic SNPs in Transporter Genes Associated with Metformin Side Effects

Metformin is a widely used and effective medication in type 2 diabetes (T2DM) as well as in polycystic ovary syndrome (PCOS). Single nucleotide polymorphisms (SNPs) contribute to the occurrence of metformin side effects. The aim of the present study was to identify intronic genetic variants modifying the occurrence of metformin side effects and to replicate them in individuals with T2DM and in women with PCOS. We performed Next Generation Sequencing (Illumina Next Seq) of 115 SNPs in a discovery cohort of 120 metformin users and conducted a systematic literature review. Selected SNPs were analysed in two independent cohorts of individuals with either T2DM or PCOS, using 5'-3'exonucleaseassay. A total of 14 SNPs in the organic cation transporters (OCTs) showed associations with side effects in an unadjusted binary logistic regression model, with eight SNPs remaining significantly associated after appropriate adjustment in the discovery cohort. Five SNPs were confirmed in a combined analysis of both replication cohorts but showed different association patterns in subgroup analyses. In an unweighted polygenic risk score (PRS), the risk for metformin side effects increased with the number of risk alleles. Intronic SNPs in the OCT cluster contribute to the development of metformin side effects in individuals with T2DM and in women with PCOS and are therefore of interest for personalized therapy options.

An Introductory Tutorial on Cardiovascular Pharmacogenetics for Healthcare Providers

Pharmacogenetics can improve clinical outcomes by reducing adverse drug effects and enhancing therapeutic efficacy for commonly used drugs that treat a wide range of cardiovascular diseases. One of the major barriers to the clinical implementation of cardiovascular pharmacogenetics is limited education on this field for current healthcare providers and students. The abundance of pharmacogenetic literature underscores its promise, but it can also be challenging to learn such a wealth of information. Moreover, current clinical recommendations for cardiovascular pharmacogenetics can be confusing because they are outdated, incomplete, or inconsistent. A myriad of misconceptions about the promise and feasibility of cardiovascular pharmacogenetics among healthcare providers also has halted clinical implementation. Therefore, the main goal of this tutorial is to provide introductory education on the use of cardiovascular pharmacogenetics in clinical practice. The target audience is any healthcare provider (or student) with patients that use or have indications for cardiovascular drugs. This tutorial is organized into the following 6 steps: (1) understand basic concepts in pharmacogenetics; (2) gain foundational knowledge of cardiovascular pharmacogenetics; (3) learn the different organizations that release cardiovascular pharmacogenetic guidelines and recommendations; (4) know the current cardiovascular drugs/drug classes to focus on clinically and the supporting evidence; (5) discuss an example patient case of cardiovascular pharmacogenetics; and (6) develop an appreciation for emerging areas in cardiovascular pharmacogenetics. Ultimately, improved education among healthcare providers on cardiovascular pharmacogenetics will lead to a greater understanding for its potential in improving outcomes for a leading cause of morbidity and mortality.

In-silico mining to glean SNPs of pharmaco-clinical importance: an investigation with reference to the Indian populated SNPs

Drugs pharmacology is defined by pharmacokinetics and pharmacodynamics and both of them are affected by genetic variability. Genetic variability varies from population to population, and sometimes even within the population, it exists. Single nucleotide polymorphisms (SNPs) are one of the major genetic variability factors which are found to be associated with the pharmacokinetics and pharmacodynamics process of a drug and are responsible for variable drug response and clinical phenotypes. Studies of SNPs can help to perform genome-wide association studies for their association with pharmacological and clinical events, at the same time; their information can direct genome-wide association studies for their use as biomarkers. With the aim to mine and characterize Indian populated SNPs of pharmacological and clinical importance. Two hundred six candidate SNPs belonging to 43 genes were retrieved from Indian Genome Variation Database. The distribution pattern of considered SNPs was observed against all five world super-populations (AFR, AMR, EAS, EUR, and SAS). Further, their annotation was done through SNP-nexus by considering Human genome reference builds - hg38, pharmacological and clinical information was supplemented by PharmGKB and ClinVar database. At last, to find out the association between SNPs linkage disequilibrium was observed in terms of r2. Overall, the study reported 53 pharmaco-clinical active SNPs and found 24 SNP-pairs as potential markers, and recommended their clinical and experimental validation.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00154-4.

Vitamin D and Multiple Myeloma: A Scoping Review

As the global incidence of multiple myeloma (MM) increases, the identification of modifiable risk factors for disease prevention becomes paramount. Maintaining optimal vitamin D status is a candidate for prevention efforts, based on pre-clinical evidence of a possible role in disease activity and progression. A structured scoping review was performed to identify and describe human-level research regarding the association between vitamin D and MM risk and/or prognosis. Searches of three databases (OVID-Medline, OVID-Embase, and OVID-Cochrane Library) yielded 15 included publications. Vitamin D deficiency is fairly common among patients with MM, with 42.3% of participants in the studies identified as having a vitamin D deficiency. No included publication reported on vitamin D status and the risk of developing or being newly diagnosed with MM. Possible associations with vitamin D that warrant future exploration include the incident staging of MM disease, the occurrence of peripheral neuropathy, and survival/prognosis. Vitamin D receptor (VDR) polymorphisms associated with MM also warrant further investigation. Overall, this scoping review was effective in mapping the research regarding vitamin D and MM and may help support new hypotheses to better describe this association and to better address identified knowledge gaps in the literature.

Considerations for Embedding Inclusive Research Principles in the Design and Execution of Clinical Trials

There is a growing recognition that the clinical research enterprise has a diversity problem, given that many clinical trials recruit historically marginalized individuals or patients reflective of real-world data at a rate that is far below the incidence and prevalence of the disease for which the investigational therapy or device is targeting. This lack of diversity in clinical research participation can obscure the safety and efficacy of drug therapies and limits our collective ability to develop effective treatments for all patients, leading to even wider health disparities. This review article provides an in-depth analysis of the impact of this bias on public health, along with a description of some of the barriers that prevent historically marginalized populations from participating in clinical research. Some practical solutions that can be employed to increase diversity in clinical trial participation are also discussed, including the crucial role clinical trial sponsors, research organizations, patients, and caregivers need to play in supporting the industry to achieve this ambitious but necessary goal.

Automation: A revolutionary vision of artificial intelligence in theranostics

The last two decades have witnessed an extraordinary evolution of automation and artificial intelligence (AI), which has become an integral part of our daily lives. Lately, AI has also been assimilated in the field of medicine to upgrade overall healthcare system and encourage personalized treatment. Theranostics literally meaning combination of diagnosis and therapeutics, is a targeted pharmacotherapy, based on specific targeted diagnostic tests. Numerous theranostic agents/biomarkers are available which can identify the most beneficial treatment, correct dose or predict response to a medicine, thus, maximizing drug efficacy, minimizing toxicity and providing informed treatment choice. For instance, a statistics based Cluster-FLIM technology provides precise data on drug-receptor binding behavior in biological tissues using fluorescence real experimental imaging. Automated Idylla™ qPCR System is another approach in oncology to determine the EGFR mutations at initial stage as well as during the treatment and also assists the oncologist in designing the treatment protocol. Recent incorporation of automation and AI in theranostics has brought a drastic change in early detection and treatment protocols for various diseases such as cancer and diabetes. Also, it leads to quick analysis of number of diverse experimental datum with accuracy. The approach mainly uses computer algorithms to unveil relevant and significant information from clinical data, thereby assisting in making accurate, logical and pertinent decisions. This review highlights the emerging uses/role of automation and AI in theranostics, technical difficulties and focuses on its future prospects to facilitate a patient specific, reliable and efficient pharmacotherapy.

Vitamin D Receptor Gene Polymorphisms Affect Osteoporosis-Related Traits and Response to Antiresorptive Therapy

The present study analyzed the effect of vitamin D receptor (VDR) gene polymorphisms (ApaI, TaqI, BsmI, FokI, and Cdx2) on bone mineral density (BMD), biochemical parameters and bone turnover markers, fracture prevalence, and response to three types of antiresorptive therapy (estrogen-progesterone, raloxifene, and ibandronate) in 356 postmenopausal women from Slovakia. Association analysis revealed a significant effect of BsmI polymorphism on lumbar spine BMD, serum osteocalcin (OC), and β-CrossLaps levels. While ApaI and Cdx2 polymorphisms were associated with OC and alkaline phosphatase, TaqI polymorphism affected all turnover markers. ApaI, TaqI, and BsmI genotypes increased the risk of spinal, radial, or total fractures with odds ratios ranging from 2.03 to 3.17. Each of therapy types evaluated had a beneficial effect on all osteoporosis-related traits; however, the VDR gene affected only ibandronate and raloxifene treatment. ApaI/aa, TaqI/TT, and BsmI/bb genotypes showed a weaker or no response to ibandronate therapy in femoral and spinal BMD. The impact of aforementioned polymorphisms on turnover markers was also genotype dependent. On the contrary, only TaqI and BsmI polymorphisms influenced raloxifene therapy, even only in lumbar spine BMD. These results point to the potential of using the VDR gene in personalized pharmacotherapy of osteoporosis.

Pharmacogenomic variabilities in geo-ancestral subpopulations and their clinical implications: Results of collaborations with Hmong in the United States

Underrepresentation of subpopulations within geo-ancestral groups engaged in research can exacerbate health disparities and impair progress toward personalized medicine. This is particularly important when implementing pharmacogenomics which uses genomic-based sources of variability to guide medication selection and dosing. This mini-review focuses on pharmacogenomic findings with Hmong in the United States and their potential clinical implications. By actively engaging Hmong community in pharmacogenomic-based research, several clinically relevant differences in allele frequencies were observed within key pharmacogenes such as CYP2C9 and CYP2C19 in Hmong compared to those in either East Asians or Europeans. Additionally, using state-of-the-art genome sequencing approaches, Hmong appear to possess novel genetic variants within CYP2D6, a critical pharmacogene affecting pharmacokinetics of a broad range of medications. The allele frequency differences and novel alleles in Hmong have translational impact and real-world clinical consequences. For example, Hmong patients exhibited a lower warfarin stable dose requirement compared to East Asian patients. This was predicted based on Hmong's unique genetic and non-genetic factors and confirmed using real-world data from clinical practice settings. By presenting evidence of the genetic uniqueness and its translational impact within subpopulations, such as the Hmong, we hope to inspire greater inclusion of other geo-ancestrally underrepresented subpopulations in pharmacogenomic-based research.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Early Insights From a Pharmacogenomic-Enriched Comprehensive Medication Management Program Implementation in an Adult Employee Population

OBJECTIVES

The aims of the study are to assess adoption of a pharmacogenomic-enriched comprehensive medication management program in a self-insured employer setting and to better understand medication risks that affect employees.

METHODS

Employees were identified to be at high risk of medication mismanagement and were subsequently provided with a program and process to improve their health. DNA testing, a clinical decision support system, and pharmacists were used to identify medication safety and effectiveness issues and to recommend appropriate changes.

RESULTS

A total of 10.6% of the invited employees enrolled in the program. Actionable recommendations were suggested by pharmacists for 85.8% of employees who completed the program, averaging 5.2 recommendations per person.

CONCLUSIONS

Implementation of a PGx + CMM program in a self-insured employer setting is feasible, detects risks in prescription regimens, and offers opportunities to improve medication management and reduce the burden of healthcare expenses.

Biological Psychiatry in Displaced Populations: What We Know, and What We Need to Begin to Learn

Conflict and climate change continue to displace millions of people, who experience unique trauma and stressors as they resettle in host countries. Both children and adults who are forcibly displaced, or choose to migrate, experience posttraumatic stress disorder, anxiety, depression, and other mental health conditions at higher rates than the general population. This may be attributed to severe, cumulative stress and trauma (largely interpersonal traumas); discrimination and harassment in host countries; and structural barriers to accessing and addressing mental health concerns, including clinician availability, language barriers, cultural differences, geographic accessibility, health care access, and stigma. Despite high exposure to and clinical impact of such experiences, and despite representing 1% of the world population, forcibly displaced people are underrepresented in neuroscientific research. The availability of such literature and research findings is significant in understanding the unique genetic and cultural aspects of trauma- and stress-related mental health, advocacy, reducing stigma, informing prevention, and treatment. The present work aimed to explore how the field of neuroscience can address mental health equity for individuals who have been uprooted in relation to land, with a focus on refugee populations. We offer practical suggestions on how to improve research in this area and narrow the gap in knowledge.

Patients’ perception of the pharmacovigilance system: A pre-diagnostic and post-interventional cross-sectional survey

Background and objective: The risk of adverse reactions necessitated the pharmacovigilance system for patient safety. A literature search documented better health literacy of patients through intervention. This investigation aims to assess the perception and the effect of an intervention on patients regarding adverse reactions caused by drugs.

Methods: A pre-diagnostic and post-interventional cross-sectional investigation was designed with a sample size of 423 patients in hospitals of Islamabad. The proportion of patients was selected based on a stratified probability technique. A prevalidated tool was used to collect the response twice through a health promotion brochure with counseling, which was applied as an intervention.

Results: The outcome of the investigation revealed that the prerequisite of the pharmacovigilance center in the hospital among respondents was improved significantly by 41.2% after intervention. Knowledge, communication, and practice were significantly different with respect to gender. There was a moderate Pearson correlation between diagnostic and interventional responses of patient’s knowledge of adverse reactions by drugs (r = 0.66, p &lt; 0.01) and patient’s communication in pharmacovigilance (r = 0.62, p &lt; 0.01) and a strong correlation between diagnostic and interventional responses of patient’s practice in the pharmacovigilance system (r = 0.72, p &lt; 0.01).

Conclusion: The finding of the investigation provided evidence that patient awareness was significantly improved by the health promotion model. Patient participation in the reporting of adverse reactions of drugs will complement the hospital staff reporting. These reports will construct an authentic, cross-checked database for rational drug safety practices in Pakistan.

Bringing pharmacomicrobiomics to the clinic through well-designed studies

Pharmacomicrobiomic studies investigate drug-microbiome interactions, such as the effect of microbial variation on drug response and disposition. Studying and understanding the interactions between the gut microbiome and drugs is becoming increasingly relevant to clinical practice due to its potential for avoiding adverse drug reactions or predicting variability in drug response. The highly variable nature of the human microbiome presents significant challenges to assessing microbes' influence. Studies aiming to explore drug-microbiome interactions should be well-designed to account for variation in the microbiome over time and collect data on confounders such as diet, disease, concomitant drugs, and other environmental factors. Here, we assemble a set of important considerations and recommendations for the methodological features required for performing a pharmacomicrobiomic study in humans with a focus on the gut microbiome. Consideration of these factors enable discovery, reproducibility, and more accurate characterization of the relationships between a given drug and the microbiome. Furthermore, appropriate interpretation and dissemination of results from well-designed studies will push the field closer to clinical relevance and implementation.

A blockchain-based framework to support pharmacogenetic data sharing

The successful implementation of pharmacogenetics (PGx) into clinical practice requires patient genomic data to be shared between stakeholders in multiple settings. This creates a number of barriers to widespread adoption of PGx, including privacy concerns related to the storage and movement of identifiable genomic data. Informatic solutions that support secure and equitable data access for genomic data are therefore important to PGx. Here we propose a methodology that uses smart contracts implemented on a blockchain-based framework, PGxChain, to address this issue. The design requirements for PGxChain were identified through a systematic literature review, identifying technical challenges and barriers impeding the clinical implementation of pharmacogenomics. These requirements included security and privacy, accessibility, interoperability, traceability and legal compliance. A proof-of-concept implementation based on Ethereum was then developed that met the design requirements. PGxChain's performance was examined using Hyperledger Caliper for latency, throughput, and transaction success rate. The findings clearly indicate that blockchain technology offers considerable potential to advance pharmacogenetic data sharing, particularly with regard to PGx data security and privacy, large-scale accessibility of PGx data, PGx data interoperability between multiple health care providers and compliance with data-sharing laws and regulations.

Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum

India confines more than 17% of the world’s population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.

Coriell Life Sciences: empowering the most precise medical care for a healthier world

Since its founding in 2013, Coriell Life Sciences (CLS) has built technologies leveraging genetic testing to empower physicians and has invested in research that advances the field of personalized medicine. The company focuses on development, implementation and research with expertise in medication safety, pharmacogenomics, infectious diseases and healthcare analytics. CLS works with healthcare institutions, laboratories, pharmacy benefit management companies, self-insured employers and public sector entities and actively contributes to scientific and clinical consortia. This overview summarizes the CLS service architecture and delivery capabilities for medication safety and risk reporting for pharmacogenomics, comprehensive medication management and infectious diseases. It includes the development and ongoing curation of genetic and non-genetic knowledge repositories, technology infrastructure and end points and research endeavors and it reviews economic, clinical and humanistic outcomes of CLS’ pharmacogenomics-enriched comprehensive management program.

Leveraging a High-Throughput Screening Method to Identify Mechanisms of Individual Susceptibility Differences in a Genetically Diverse Zebrafish Model

Understanding the mechanisms behind chemical susceptibility differences is key to protecting sensitive populations. However, elucidating gene-environment interactions (GxE) presents a daunting challenge. While mammalian models have proven useful, problems with scalability to an enormous chemical exposome and clinical translation faced by all models remain; therefore, alternatives are needed. Zebrafish (Danio rerio) have emerged as an excellent model for investigating GxE. This study used a combined bioinformatic and experimental approach to probe the mechanisms underlying chemical susceptibility differences in a genetically diverse zebrafish population. Starting from high-throughput screening (HTS) data, a genome-wide association study (GWAS) using embryonic fish exposed to 0.6 μM Abamectin revealed significantly different effects between individuals. A hypervariable region with two distinct alleles–one with G at the SNP locus (GG) and one with a T and the 16 bp deletion (TT)–associated with differential susceptibility was found. Sensitive fish had significantly lower sox7 expression. Due to their location and the observed expression differences, we hypothesized that these sequences differentially regulate sox7. A luciferase reporter gene assay was used to test if these sequences, alone, could lead to expression differences. The TT allele showed significantly lower expression than the GG allele in MCF-7 cells. To better understand the mechanism behind these expression differences, predicted transcription factor binding differences between individuals were compared in silico, and several putative binding differences were identified. EMSA was used to test for binding differences in whole embryo protein lysate to investigate these TF binding predictions. We confirmed that the GG sequence is bound to protein in zebrafish. Through a competition EMSA using an untagged oligo titration, we confirmed that the GG oligo had a higher binding affinity than the TT oligo, explaining the observed expression differences. This study identified differential susceptibility to chemical exposure in a genetically diverse population, then identified a plausible mechanism behind those differences from a genetic to molecular level. Thus, an HTS-compatible zebrafish model is valuable and adaptable in identifying GxE mechanisms behind susceptibility differences to chemical exposure.

Different responses to risperidone treatment in Schizophrenia: a multicenter genome-wide association and whole exome sequencing joint study

Risperidone is routinely used in the clinical management of schizophrenia, but the treatment response is highly variable among different patients. The genetic underpinnings of the treatment response are not well understood. We performed a pharmacogenomic study of the treatment response to risperidone in patients with schizophrenia by using a SNP microarray -based genome-wide association study (GWAS) and whole exome sequencing (WES)-based GWAS. DNA samples were collected from 189 patients for the GWAS and from 222 patients for the WES after quality control in multiple centers of China. Antipsychotic response phenotypes of patients who received eight weeks of risperidone treatment were quantified with percentage change on the Positive and Negative Syndrome Scale (PANSS). The GWAS revealed a significant association between several SNPs and treatment response, such as three GRM7 SNPs (rs141134664, rs57521140, and rs73809055). Gene-based analysis in WES revealed 13 genes that were associated with antipsychotic response, such as GPR12 and MAP2K3. We did not identify shared loci or genes between GWAS and WES, but association signals tended to cluster into the GPCR gene family and GPCR signaling pathway, which may play an important role in the treatment response etiology. This study may provide a research paradigm for pharmacogenomic research, and these data provide a promising illustration of our potential to identify genetic variants underlying antipsychotic responses and may ultimately facilitate precision medicine in schizophrenia.

Analysis of a panel-based pharmacogenomics testing program among members of a commercial and Medicare client of a pharmacy benefits manager

BACKGROUND: Although the field of pharmacogenomics (PGx) has existed for decades, use of pharmacogenomic information by providers to optimize medication therapy for patients has had relatively slow adoption. There are many factors that have contributed to the slow adoption of PGx testing, but it is partially due to a lack of coverage by payers. If PGx testing is covered by payers, frequently only testing of a specific gene is covered, rather than a panel of many genes. As a result, little is known about how coverage of a panel-based PGx test will affect a member's medication therapy. OBJECTIVES: To determine how giving providers specific medication optimization recommendations, based on results of a panel-based PGx test, impacted members' medication regimens. METHODS: Pharmacy claims data were retrospectively reviewed for this exploratory study. Members who participated in PGx testing were in the intervention group and members who chose not to participate in the PGx testing, but who were eligible to participate, were in the control group. PGx test results, including suggested medication changes, were mailed to providers. To determine if providers adopted the suggested medication changes, pharmacy claims data were analyzed retrospectively for the 4-month period preceding and following the date from which recommendations were provided to prescribers. RESULTS: Of the 101 members included in the analysis, 50 were in the intervention group and 51 were in the control group. In the intervention group, members were taking in a total of 352 medications; 165 of the medications had PGx guidance. Based on the PGx test results, 62 of these medications (37.6%) had recommendations. Of members who received PGx testing, 76% had at least 1 recommended change. When pharmacist recommendations were made, a change was made to the medication 27% of the time. There was a statistically significant difference between the number of medication changes in the PGx group and the control group (P = 0.024). CONCLUSIONS: Recommendations based on PGx testing can lead to changes in medications and an optimized medication regimen for members. DISCLOSURES: The authors have no conflicts to disclose that may present a potential conflict of interest.

SWAAT Bioinformatics Workflow for Protein Structure-Based Annotation of ADME Gene Variants

Recent genomic studies have revealed the critical impact of genetic diversity within small population groups in determining the way individuals respond to drugs. One of the biggest challenges is to accurately predict the effect of single nucleotide variants and to get the relevant information that allows for a better functional interpretation of genetic data. Different conformational scenarios upon the changing in amino acid sequences of pharmacologically important proteins might impact their stability and plasticity, which in turn might alter the interaction with the drug. Current sequence-based annotation methods have limited power to access this type of information. Motivated by these calls, we have developed the Structural Workflow for Annotating ADME Targets (SWAAT) that allows for the prediction of the variant effect based on structural properties. SWAAT annotates a panel of 36 ADME genes including 22 out of the 23 clinically important members identified by the PharmVar consortium. The workflow consists of a set of Python codes of which the execution is managed within Nextflow to annotate coding variants based on 37 criteria. SWAAT also includes an auxiliary workflow allowing a versatile use for genes other than ADME members. Our tool also includes a machine learning random forest binary classifier that showed an accuracy of 73%. Moreover, SWAAT outperformed six commonly used sequence-based variant prediction tools (PROVEAN, SIFT, PolyPhen-2, CADD, MetaSVM, and FATHMM) in terms of sensitivity and has comparable specificity. SWAAT is available as an open-source tool.

Role of the gut microbiome in cardiovascular drug response: The potential for clinical application

Response to cardiovascular drugs can vary greatly between individuals, and the role of the microbiome in this variability is being increasingly appreciated. Recent evidence indicates that bacteria and other microbes are responsible for direct and indirect effects on drug efficacy and toxicity. Pharmacomicrobiomics aims to uncover variability in drug response due to microbes in the human body, which may alter drug disposition through microbial metabolism, interference by microbial metabolites, or modification of host enzymes. In this review, we present recent advances in our understanding of the interplay between microbes, host metabolism, and cardiovascular drugs. We report numerous cardiovascular drugs with evidence of, or potential for, gut-microbe interactions. However, the effects of gut microbiota on many cardiovascular drugs are yet uninvestigated. Finally, we consider potential clinical applications for the described findings.

Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy

Rapid methodological advances in statistical and computational genomics have enabled researchers to better identify and interpret both rare and common variants responsible for complex human diseases. As we continue to see an expansion of these advances in the field, it is now imperative for researchers to understand the resources and methodologies available for various data types and study designs. In this review, we provide an overview of recent methods for identifying rare and common variants and understanding their roles in disease etiology. Additionally, we discuss the strategy, challenge, and promise of gene therapy. As computational and statistical approaches continue to improve, we will have an opportunity to translate human genetic findings into personalized health care.

Unravelling Mechanisms of Doxorubicin-Induced Toxicity in 3D Human Intestinal Organoids

Doxorubicin is widely used in the treatment of different cancers, and its side effects can be severe in many tissues, including the intestines. Symptoms such as diarrhoea and abdominal pain caused by intestinal inflammation lead to the interruption of chemotherapy. Nevertheless, the molecular mechanisms associated with doxorubicin intestinal toxicity have been poorly explored. This study aims to investigate such mechanisms by exposing 3D small intestine and colon organoids to doxorubicin and to evaluate transcriptomic responses in relation to viability and apoptosis as physiological endpoints. The in vitro concentrations and dosing regimens of doxorubicin were selected based on physiologically based pharmacokinetic model simulations of treatment regimens recommended for cancer patients. Cytotoxicity and cell morphology were evaluated as well as gene expression and biological pathways affected by doxorubicin. In both types of organoids, cell cycle, the p53 signalling pathway, and oxidative stress were the most affected pathways. However, significant differences between colon and SI organoids were evident, particularly in essential metabolic pathways. Short time-series expression miner was used to further explore temporal changes in gene profiles, which identified distinct tissue responses. Finally, in silico proteomics revealed important proteins involved in doxorubicin metabolism and cellular processes that were in line with the transcriptomic responses, including cell cycle and senescence, transport of molecules, and mitochondria impairment. This study provides new insight into doxorubicin-induced effects on the gene expression levels in the intestines. Currently, we are exploring the potential use of these data in establishing quantitative systems toxicology models for the prediction of drug-induced gastrointestinal toxicity.

Evaluation of the relationship of IL-17A and IL-17F gene polymorphisms with the response to treatment in psoriatic patients using biological drugs: a case-control study in patients in Eastern Turkey

Introduction

IL-17A and IL-17F cytokines have important roles in the pathogenesis of psoriasis.

Aim

To examine the associations of IL-17A rs2275913 and IL-17F rs763780 variants with the development of psoriasis and whether these polymorphisms affect the responsiveness of biological agents.

Material and methods

In our case-controlled study, which included 83 psoriatic patients who were treated with different biological agents and 69 healthy controls, we genotyped IL-17A rs2275913 and IL-17F rs763780 variants using TaqMan probes.

Results

We did not observe statistically significant changes in genotype frequencies of IL-17A rs2275913 (p = 0.922) and IL-17F rs763780 (p = 0.621) variants between patient and control groups. Although we did not find any association between these polymorphisms and the development of psoriasis, statistical analyses showed that individuals with the IL-17A AA genotype had shorter disease duration (9.09 ±6.82, p = 0.020) and AA genotype frequency was higher in patients who used single conventional treatment (34.6%; p = 0.025). IL17A/rs2275913 variant in terms of disease duration, it was observed that individuals with AA genotype had a shorter disease duration (less than 10 years) (p = 0.009). For patients with PASI90 and PASI100 response, the IL-17A AA genotype was significantly higher (p = 0.015). On the other hand, we did not detect any statistically significant correlation between variants and response to biological agents.

Conclusions

According to our results, we may suggest that rs2275913 variant seems to be associated with disease duration, use of single conventional treatment and responsiveness of PASI90 and PASI100 however both variants have no effect on the susceptibility to psoriasis in the population of Eastern Turkey.

Estimating heritability of glycaemic response to metformin using nationwide electronic health records and population-sized pedigree

Background

Variability of response to medication is a well-known phenomenon, determined by both environmental and genetic factors. Understanding the heritable component of the response to medication is of great interest but challenging due to several reasons, including small study cohorts and computational limitations.

Methods

Here, we study the heritability of variation in the glycaemic response to metformin, first-line therapeutic agent for type 2 diabetes (T2D), by leveraging 18 years of electronic health records (EHR) data from Israel’s largest healthcare service provider, consisting of over five million patients of diverse ethnicities and socio-economic background. Our cohort consists of 80,788 T2D patients treated with metformin, with an accumulated number of 1,611,591 HbA1C measurements and 4,581,097 metformin prescriptions. We estimate the explained variance of glycated hemoglobin (HbA1c%) reduction due to inheritance by constructing a six-generation population-size pedigree from national registries and linking it to medical health records.

Results

Using Linear Mixed Model-based framework, a common-practice method for heritability estimation, we calculate a heritability measure of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=12.6 \%$$\end{document}h2=12.6% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.1 \%\! -\!19.1 \%$$\end{document}6.1%−19.1%) for absolute reduction of HbA1c% after metformin treatment in the entire cohort, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=21.0 \%$$\end{document}h2=21.0% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7.8 \%\! -\!34.4 \%$$\end{document}7.8%−34.4%) for males and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${h}^{2}=22.9 \%$$\end{document}h2=22.9% (95% CI, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$10.0 \%\! -\!35.7 \%$$\end{document}10.0%−35.7%) in females. Results remain unchanged after adjusting for pre-treatment HbA1c%, and in proportional reduction of HbA1c%.

Conclusions

To the best of our knowledge, our work is the first to estimate heritability of drug response using solely EHR data combining a pedigree-based kinship matrix. We demonstrate that while response to metformin treatment has a heritable component, most of the variation is likely due to other factors, further motivating non-genetic analyses aimed at unraveling metformin’s action mechanism.

Individuals in a population might respond differently to the same medication and this phenomenon is commonly attributed to either genes or the environment. Here, we studied the familial aspects of the response to metformin, a medication used in the treatment of type 2 diabetes. We combined information from 18 years of medical records identifying newly treated patients with type 2 diabetes with information about how the trait was inherited within their families. We calculated a metric that tells us how well differences in people’s genes account for differences in their traits, and demonstrate that although the difference in response to metformin is in part explained by the genes people with type 2 diabetes inherit, most of it is not explained by genes. This finding contributes to a better understanding of differences in metformin response and might help inform treatment in future.

Kalka and Gavrieli et al. assessed the heritability of variation in the glycaemic response to metformin by leveraging electronic health records data gathered from a large cohort of patients with diabetes and combining it with pedigree information. The authors show that although the variability in this response has a heritable component, most of it is likely non-genetic.

Genetic diversity of 'Very Important Pharmacogenes' in two South-Asian populations

Objectives

Reliable identification of population-specific variants is important for building the single nucleotide polymorphism (SNP) profile. In this study, genomic variation using allele frequency differences of pharmacologically important genes for Gujarati Indians in Houston (GIH) and Indian Telugu in the U.K. (ITU) from the 1000 Genomes Project vis-à-vis global population data was studied to understand its role in drug response.

Methods

Joint genotyping approach was used to derive variants of GIH and ITU independently. SNPs of both these populations with significant allele frequency variation (minor allele frequency ≥ 0.05) with super-populations from the 1000 Genomes Project and gnomAD based on Chi-square distribution with p-value of ≤ 0.05 and Bonferroni’s multiple adjustment tests were identified. Population stratification and fixation index analysis was carried out to understand genetic differentiation. Functional annotation of variants was carried out using SnpEff, VEP and CADD score.

Results

Population stratification of VIP genes revealed four clusters viz., single cluster of GIH and ITU, one cluster each of East Asian, European, African populations and Admixed American was found to be admixed. A total of 13 SNPs belonging to ten pharmacogenes were identified to have significant allele frequency variation in both GIH and ITU populations as compared to one or more super-populations. These SNPs belong to VKORC1 (rs17708472, rs2359612, rs8050894) involved in Vitamin K cycle, cytochrome P450 isoforms CYP2C9 (rs1057910), CYP2B6 (rs3211371), CYP2A2 (rs4646425) and CYP2A4 (rs4646440); ATP-binding cassette (ABC) transporter ABCB1 (rs12720067), DPYD1 (rs12119882, rs56160474) involved in pyrimidine metabolism, methyltransferase COMT (rs9332377) and transcriptional factor NR1I2 (rs6785049). SNPs rs1544410 (VDR), rs2725264 (ABCG2), rs5215 and rs5219 (KCNJ11) share high fixation index (≥ 0.5) with either EAS/AFR populations. Missense variants rs1057910 (CYP2C9), rs1801028 (DRD2) and rs1138272 (GSTP1), rs116855232 (NUDT15); intronic variants rs1131341 (NQO1) and rs115349832 (DPYD) are identified to be ‘deleterious’.

Conclusions

Analysis of SNPs pertaining to pharmacogenes in GIH and ITU populations using population structure, fixation index and allele frequency variation provides a premise for understanding the role of genetic diversity in drug response in Asian Indians.

Population pharmacokinetic model selection assisted by machine learning

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learning algorithms. We compared the classical pharmacometric approach with two machine learning methods, genetic algorithm and neural networks, in different scenarios based on simulated pharmacokinetic data. Genetic algorithm performance was assessed using a fitness function based on log-likelihood, whilst neural networks were trained using mean square error or binary cross-entropy loss. Machine learning provided a selection based only on statistical rules and achieved accurate selection. The minimization process of genetic algorithm was successful at allowing the algorithm to select plausible models. Neural network classification tasks achieved the most accurate results. Neural network regression tasks were less precise than neural network classification and genetic algorithm methods. The computational gain obtained by using machine learning was substantial, especially in the case of neural networks. We demonstrated that machine learning methods can greatly increase the efficiency of pharmacokinetic population model selection in case of large datasets or complex models requiring long run-times. Our results suggest that machine learning approaches can achieve a first fast selection of models which can be followed by more conventional pharmacometric approaches.

The need to shift pharmacogenetic research from candidate gene to genome-wide association studies

The primary research approach in pharmacogenetics has been candidate gene association studies (CGAS), but pharmacogenomic genome-wide association studies (GWAS) are becoming more common. We are now at a critical juncture when the results of those two research approaches, CGAS and GWAS, can be compared in pharmacogenetics. We analyzed publicly available databases of pharmacogenetic CGAS and GWAS (i.e., the Pharmacogenomics Knowledgebase [PharmGKB^®] and the NHGRI-EBI GWAS catalog) and the vast majority of variants (98%) and genes (94%) discovered in pharmacogenomic GWAS were novel (i.e., not previously studied CGAS). Therefore, pharmacogenetic researchers are not selecting the right candidate genes in the vast majority of CGAS, highlighting a need to shift pharmacogenetic research efforts from CGAS to GWAS.

Pharmacometabolomics by NMR in Oncology: A Systematic Review

Pharmacometabolomics (PMx) studies aim to predict individual differences in treatment response and in the development of adverse effects associated with specific drug treatments. Overall, these studies inform us about how individuals will respond to a drug treatment based on their metabolic profiles obtained before, during, or after the therapeutic intervention. In the era of precision medicine, metabolic profiles hold great potential to guide patient selection and stratification in clinical trials, with a focus on improving drug efficacy and safety. Metabolomics is closely related to the phenotype as alterations in metabolism reflect changes in the preceding cascade of genomics, transcriptomics, and proteomics changes, thus providing a significant advance over other omics approaches. Nuclear Magnetic Resonance (NMR) is one of the most widely used analytical platforms in metabolomics studies. In fact, since the introduction of PMx studies in 2006, the number of NMR-based PMx studies has been continuously growing and has provided novel insights into the specific metabolic changes associated with different mechanisms of action and/or toxic effects. This review presents an up-to-date summary of NMR-based PMx studies performed over the last 10 years. Our main objective is to discuss the experimental approaches used for the characterization of the metabolic changes associated with specific therapeutic interventions, the most relevant results obtained so far, and some of the remaining challenges in this area.

A Decade of Pharmacogenetic Studies in Jordan: A Systemic Review

The aim of this study was to perform a systematic overview of the pharmacogenetic studies conducted in Jordan. A structured search of Medline was conducted for articles over the last decade (January 2010-July 2020). Studies were classified by design, sample size, drug-gene combination, and the significance of the results. Thirty-two studies met the criteria for review. Most pharmacogenomic studies had a case-only design (n = 23). Only five studies included >500 participants. The total number of genetic variants in all studies was one hundred fifteen, which were found in forty genes, including dynamic (n = 27), and kinetic (n = 9) genes. The most commonly studied drugs were within the hematology and cardiology therapeutic areas and included statins, warfarin, aspirin, and clopidogrel. Most studies (n = 18) reported results with mixed p values [<0.05 and >0.05]. Pharmacogenomic research in Jordan is still in its infancy and is limited mainly to replication attempts. The need for standardization is imperative, especially in developing countries with scarce funding resources.

NR3C2 genotype is associated with response to spironolactone in diastolic heart failure patients from the Aldo-DHF trial

STUDY OBJECTIVE

This study aimed to determine if variants in NR3C2, which codes the target protein of spironolactone, or CYP11B2, which is involved in aldosterone synthesis, were associated with spironolactone response, focused on the primary end point of diastolic function (E/e'), in Aldosterone Receptor Blockade in Diastolic Heart Failure (Aldo-DHF) participants.

DESIGN

Post-hoc genetic analysis.

DATA SOURCE

Data and samples were derived from the multi-center, randomized, double-blind, placebo-controlled Aldo-DHF trial.

PATIENTS

Aldo-DHF participants treated with spironolactone (n = 184) or placebo (n = 178) were included.

INTERVENTION

Participants were genotyped for NR3C2 rs5522, NR3C2 rs2070951 and CYP11B2 rs1799998 via pyrosequencing.

MEASUREMENTS

In the placebo and spironolactone arms, separate multivariable linear regression analyses were performed for change in E/e' with each single nucleotide polymorphism (SNP), adjusted for age, sex, and baseline E/e'. To discern potential mechanisms of a genotype effect, associated SNPs were further examined for their association with change in blood pressure, circulating procollagen type III N-terminal peptide (PIIINP), and left atrial area.

MAIN RESULTS

Carriers of the rs5522 G allele in the placebo arm had a greater increase in E/e' over the 12-month course of the trial compared to noncarriers (β = 1.10; 95% confidence interval [CI]: 0.05-2.16; p = 0.04). No corresponding E/e' worsening by rs5522 genotype was observed in the spironolactone arm. None of the other genotypes were associated with change in E/e'. Compared to noncarriers, rs5522 G carriers also had a greater increase in left atrial area with placebo (β = 0.83; 95% CI: 0.17-1.48; p = 0.01) and a greater reduction in diastolic blood pressure with spironolactone (β = -3.56; 95% CI: -6.73 to -0.39; p = 0.03). Serum PIIINP levels were similar across rs5522 genotypes.

CONCLUSIONS

Our results suggest that spironolactone attenuates progression of diastolic dysfunction associated with the NR3C2 rs5522 G allele. Validation of our findings is needed.

Assessment of the need for pharmacogenomics education among pharmacists in the West Bank of Palestine

BACKGROUND

Pharmacogenomics testing aims to optimise therapy and reduce the inter-individual variation in drug response. One of the major barriers against the implementation of pharmacogenomics testing is the low level of knowledge on the topic.

AIMS

This study aimed to evaluate the need for pharmacogenomics education among pharmacists in the West Bank of Palestine.

METHODS

This study was cross-sectional and included 370 pharmacists, among different cities in the West Bank of Palestine between October and December 2020. The questionnaire consisted of 25 close-ended questions that evaluated the exposure to pharmacogenomics education, attitude toward the role of pharmacogenomics testing in clinical practice and self-capability of pharmacists in pharmacogenomics testing.

RESULTS

It was found that 60% of the respondents disagreed that pharmacogenomics was an integral part of the pharmacy school curriculum and/or experiential education. The vast majority of the respondents (94%) agreed that pharmacists should be required to have some knowledge of pharmacogenomics. The majority of the respondents (88.6%) believe that pharmacogenomics testing will improve pharmacists' ability to more effectively control drug therapy expenditures. However, only 38% of the respondents could identify medications that require pharmacogenomics testing, and only 35.1% could identify reliable sources of information regarding pharmacogenomics for healthcare providers and patients.

CONCLUSION

It is seen from the results of this study that there is a high need to learn about pharmacogenomics testing, which can help the pharmacists make pharmacotherapy decisions. Additionally, current pharmacists have low self-confidence in making decisions depending on the results of pharmacogenomics testing. It is recommended to increase the exposure of pharmacogenomics knowledge by including the subject in courses and workshops in pharmacy school curricula in the West Bank of Palestine.

Frequency of Important CYP450 Enzyme Gene Polymorphisms in the Iranian Population in Comparison with Other Major Populations: A Comprehensive Review of the Human Data

Genetic polymorphisms in cytochrome P450 genes can cause alteration in metabolic activity of clinically important medicines. Thus, single nucleotide variants (SNVs) and copy number variations (CNVs) in CYP genes are leading factors of drug pharmacokinetics and toxicity and form pharmacogenetics biomarkers for drug dosing, efficacy, and safety. The distribution of cytochrome P450 alleles differs significantly between populations with important implications for personalized drug therapy and healthcare programs. To provide a meta-analysis of CYP allele polymorphisms with clinical importance, we brought together whole-genome and exome sequencing data from 800 unrelated individuals of Iranian population (100 subjects from 8 major ethnics of Iran) and 63,269 unrelated individuals of five major human populations (EUR, AMR, AFR, EAS and SAS). By integrating these datasets with population-specific linkage information, we evolved the frequencies of 140 CYP haplotypes related to 9 important CYP450 isoenzymes (CYP1A2, CYP2B6, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, CYP3A4 and CYP3A5) giving a large resource for major genetic determinants of drug metabolism. Furthermore, we evaluated the more frequent Iranian alleles and compared the dataset with the Caucasian race. Finally, the similarity of the Iranian population SNVs with other populations was investigated.

The Role of Mitochondrial DNA Variation in Drug Response: A Systematic Review

Background: The triad of drug efficacy, toxicity and resistance underpins the risk-benefit balance of all therapeutics. The application of pharmacogenomics has the potential to improve the risk-benefit balance of a given therapeutic via the stratification of patient populations based on DNA variants. A growth in the understanding of the particulars of the mitochondrial genome, alongside the availability of techniques for its interrogation has resulted in a growing body of literature examining the impact of mitochondrial DNA (mtDNA) variation upon drug response. Objective: To critically evaluate and summarize the available literature, across a defined period, in a systematic fashion in order to map out the current landscape of the subject area and identify how the field may continue to advance. Methods: A systematic review of the literature published between January 2009 and December 2020 was conducted using the PubMed database with the following key inclusion criteria: reference to specific mtDNA polymorphisms or haplogroups, a core objective to examine associations between mtDNA variants and drug response, and research performed using human subjects or human in vitro models. Results: Review of the literature identified 24 articles reporting an investigation of the association between mtDNA variant(s) and drug efficacy, toxicity or resistance that met the key inclusion criteria. This included 10 articles examining mtDNA variations associated with antiretroviral therapy response, 4 articles examining mtDNA variants associated with anticancer agent response and 4 articles examining mtDNA variants associated with antimicrobial agent response. The remaining articles covered a wide breadth of medications and were therefore grouped together and referred to as "other." Conclusions: Investigation of the impact of mtDNA variation upon drug response has been sporadic to-date. Collective assessment of the associations identified in the articles was inconclusive due to heterogeneous methods and outcomes, limited racial/ethnic groups, lack of replication and inadequate statistical power. There remains a high degree of idiosyncrasy in drug response and this area has the potential to explain variation in drug response in a clinical setting, therefore further research is likely to be of clinical benefit.

Therapeutic challenges at the preclinical level for targeted drug development for Opisthorchis viverrini-associated cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a malignant tumor of bile duct epithelium with the highest incidence found in Thailand. Some patients are considered suitable for adjuvant therapy and surgical resection is currently the curative treatment for CCA patients. Tumor recurrence is still a hurdle after treatment; hence, finding novel therapeutic strategies to combat CCA is necessary for improving outcome for patients.

AREAS COVERED

We discuss targeted therapies and other novel treatment approaches which include protein kinase inhibitors, natural products, amino acid transporter-based inhibitors, immunotherapy, and drug repurposing. We also examine the challenges of tumor heterogeneity, cancer stem cells (CSCs), the tumor microenvironment, exosomes, multiomics studies, and the potential of precision medicine.

EXPERT OPINION

Because CCA is difficult to diagnose at the early stage, the traditional treatment approaches are not effective for many patients and most tumors recur. Consequently, researchers are exploring multi-aspect molecular carcinogenesis to uncover molecular targets for further development of novel targeted drugs.

Frequency of Participation in External Quality Assessment Programs Focused on Rare Diseases: Belgian Guidelines for Human Genetics Centers

Background

Participation in quality controls, also called external quality assessment (EQA) schemes, is required for the ISO15189 accreditation of the Medical Centers of Human Genetics. However, directives on the minimal frequency of participation in genetic quality control schemes are lacking or too heterogeneous, with a possible impact on health care quality.

Objective

The aim of this project is to develop Belgian guidelines on the frequency of participation in quality controls for genetic testing in the context of rare diseases.

Methods

A group of experts analyzed 90 EQA schemes offered by accredited providers and focused on analyses used for the diagnosis of rare diseases. On that basis, the experts developed practical recommendations about the minimal frequencies of participation of the Medical Centers of Human Genetics in quality controls and how to deal with poor performances and change management. These guidelines were submitted to the Belgian Accreditation Body and then reviewed and approved by the Belgian College of Human Genetics and Rare Diseases and by the National Institute for Health and Disability Insurance.

Results

The guidelines offer a decisional algorithm for the minimal frequency of participation in human genetics EQA schemes. This algorithm has been developed taking into account the scopes of the EQA schemes, the levels of experience, and the annual volumes of the Centers of Human Genetics in the performance of the tests considered. They include three key principles: (1) the recommended annual assessment of all genetic techniques and technological platforms, if possible through EQAs covering the technique, genotyping, and clinical interpretation; (2) the triennial assessment of the genotyping and interpretation of specific germline mutations and pharmacogenomics analyses; and (3) the documentation of actions undertaken in the case of poor performances and the participation to quality control the following year. The use of a Bayesian statistical model has been proposed to help the Centers of Human Genetics to determine the theoretical number of tests that should be annually performed to achieve a certain threshold of performance (eg, a maximal error rate of 1%). Besides, the guidelines insist on the role and responsibility of the national public health authorities in the follow-up of the quality of analyses performed by the Medical Centers of Human Genetics and in demonstrating the cost-effectiveness and rationalization of participation frequency in these quality controls.

Conclusions

These guidelines have been developed based on the analysis of a large panel of EQA schemes and data collected from the Belgian Medical Centers of Human Genetics. They are applicable to other countries and will facilitate and improve the quality management and financing systems of the Medical Centers of Human Genetics.

Polymorphism in INSR Locus Modifies Risk of Atrial Fibrillation in Patients on Thyroid Hormone Replacement Therapy

Aims

Atrial fibrillation (AF) is a risk for patients receiving thyroid hormone replacement therapy. No published work has focused on pharmacogenetics relevant to thyroid dysfunction and AF risk. We aimed to assess the effect of L-thyroxine on AF risk stratified by a variation in a candidate gene.

Methods and Results

A retrospective follow-up study was done among European Caucasian patients from the Genetics of Diabetes Audit and Research in Tayside Scotland cohort (Scotland, United Kingdom). Linked data on biochemistry, prescribing, hospital admissions, demographics, and genetic biobank were used to ascertain patients on L-thyroxine and diagnosis of AF. A GWAS-identified insulin receptor-INSR locus (rs4804416) was the candidate gene. Cox survival models and sensitivity analyses by taking competing risk of death into account were used. Replication was performed in additional sample (The Genetics of Scottish Health Research register, GoSHARE), and meta-analyses across the results of the study and replication cohorts were done. We analyzed 962 exposed to L-thyroxine and 5,840 unexposed patients who were rs4804416 genotyped. The rarer G/G genotype was present in 18% of the study population. The total follow-up was up to 20 years, and there was a significant increased AF risk for patients homozygous carriers of the G allele exposed to L-thyroxine (RHR = 2.35, P = 1.6e-02). The adjusted increased risk was highest within the first 3 years of exposure (RHR = 9.10, P = 8.5e-04). Sensitivity analysis yielded similar results. Effects were replicated in GoSHARE (n = 3,190).

Conclusion

Homozygous G/G genotype at the INSR locus (rs4804416) is associated with an increased risk of AF in patients on L-thyroxine, independent of serum of free thyroxine and thyroid-stimulating hormone serum concentrations.

Fast screening of covariates in population models empowered by machine learning

One of the objectives of Pharmacometry (PMX) population modeling is the identification of significant and clinically relevant relationships between parameters and covariates. Here, we demonstrate how this complex selection task could benefit from supervised learning algorithms using importance scores. We compare various classical methods with three machine learning (ML) methods applied to NONMEM empirical Bayes estimates: random forest, neural networks (NNs), and support vector regression (SVR). The performance of the ML models is assessed using receiver operating characteristic (ROC) curves. The F1 score, which measures test accuracy, is used to compare ML and PMX approaches. Methods are applied to different scenarios of covariate influence based on simulated pharmacokinetics data. ML achieved similar or better F1 scores than stepwise covariate modeling (SCM) and conditional sampling for stepwise approach based on correlation tests (COSSAC). Correlations between covariates and the number of false covariates does not affect the performance of any method, but effect size has an impact. Methods are not equivalent with respect to computational speed; SCM is 30 and 100-times slower than NN and SVR, respectively. The results are validated in an additional scenario involving 100 covariates. Taken together, the results indicate that ML methods can greatly increase the efficiency of population covariate model building in the case of large datasets or complex models that require long run-times. This can provide fast initial covariate screening, which can be followed by more conventional PMX approaches to assess the clinical relevance of selected covariates and build the final model.

Predicting drug-metagenome interactions: Variation in the microbial β-glucuronidase level in the human gut metagenomes

Characterizing the gut microbiota in terms of their capacity to interfere with drug metabolism is necessary to achieve drug efficacy and safety. Although examples of drug-microbiome interactions are well-documented, little has been reported about a computational pipeline for systematically identifying and characterizing bacterial enzymes that process particular classes of drugs. The goal of our study is to develop a computational approach that compiles drugs whose metabolism may be influenced by a particular class of microbial enzymes and that quantifies the variability in the collective level of those enzymes among individuals. The present paper describes this approach, with microbial β-glucuronidases as an example, which break down drug-glucuronide conjugates and reactivate the drugs or their metabolites. We identified 100 medications that may be metabolized by β-glucuronidases from the gut microbiome. These medications included morphine, estrogen, ibuprofen, midazolam, and their structural analogues. The analysis of metagenomic data available through the Sequence Read Archive (SRA) showed that the level of β-glucuronidase in the gut metagenomes was higher in males than in females, which provides a potential explanation for the sex-based differences in efficacy and toxicity for several drugs, reported in previous studies. Our analysis also showed that infant gut metagenomes at birth and 12 months of age have higher levels of β-glucuronidase than the metagenomes of their mothers and the implication of this observed variability was discussed in the context of breastfeeding as well as infant hyperbilirubinemia. Overall, despite important limitations discussed in this paper, our analysis provided useful insights on the role of the human gut metagenome in the variability in drug response among individuals. Importantly, this approach exploits drug and metagenome data available in public databases as well as open-source cheminformatics and bioinformatics tools to predict drug-metagenome interactions.