Addressing Ancestry and Sex Bias in Pharmacogenomics

Biases in machine-learning models of human single-cell data

Recent machine-learning (ML)-based advances in single-cell data science have enabled the stratification of human tissue donors at single-cell resolution, promising to provide valuable diagnostic and prognostic insights. However, such insights are susceptible to biases. Here we discuss various biases that emerge along the pipeline of ML-based single-cell analysis, ranging from societal biases affecting whose samples are collected, to clinical and cohort biases that influence the generalizability of single-cell datasets, biases stemming from single-cell sequencing, ML biases specific to (weakly supervised or unsupervised) ML models trained on human single-cell samples and biases during the interpretation of results from ML models. We end by providing methods for single-cell data scientists to assess and mitigate biases, and call for efforts to address the root causes of biases.

Pharmacogenomics Tools for Precision Public Health and Lessons for Low- and Middle-Income Countries: A Scoping Review

Pharmacogenomics is the integration of genomics and pharmacology to optimize drug response and reduce side effects. In terms of personalized or individualized medicine, PGx is defined as the identification and analysis of specific genetic variants associated with particular drug treatments for each patient. Under a precision public health (PPH) approach, population-level data are analyzed to generate public health strategies. The objective of this study was to conduct a scoping review of technological tools, examining their evolution, the predominance of high-income countries in their development, and the gaps and needs for genomic data and advances in low- and middle-income countries (LMICs). This review was conducted in accordance with the ScPRISMA guidelines. A search was conducted in PubMed, Web of Science and Embase until January 2024. A total of 40 documents were selected, which revealed the continuous evolution and progressive development of pharmacogenomic tools. The technological tools developed come from high-income countries, particularly the United States, Canada, China, and several European nations, where international collaboration has been essential to maintain and expand these tools, which have evolved to keep pace with the rapid generation of genomic data. This trend shows a scarce development of technological tools for public health precision in LMICs, which evidences the need to increase investment in genomic research infrastructure in this aspect and in the development of capacities to guarantee global accessibility and boost PPH for all populations.

Discussion on the optimization of personalized medication using information systems based on pharmacogenomics: an example using colorectal cancer

Pharmacogenomics (PGx) is a powerful tool for clinical optimization of drug efficacy and safety. However, due to many factors affecting drugs in the real world, PGx still accounts for a small proportion of actual clinical application scenarios. Therefore, based on the information software, pharmacists use their professional advantages to integrate PGx into all aspects of pharmaceutical care, which is conducive to promoting the development of personalized medicine. In this paper, the establishment of an information software platform is summarized for the optimization of a personalized medication program based on PGx. Taking colorectal cancers (CRC) as an example, this paper also discusses the role of PGx in different working modes and participation in drug management of CRC patients by pharmacists with the help of information systems. Finally, we summarized the recommendations of different PGx guidelines to provide reference for the follow-up personalized pharmaceutical care.

Bridging genomics' greatest challenge: The diversity gap

Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity. Some populations have greater proportional representation in data relative to their population size and the genomic diversity present in their ancestral haplotypes. As insights from genomics become increasingly integrated into evidence-based medicine, strategic inclusion and effective mechanisms to ensure representation of global genomic diversity in datasets are imperative.

Genetic ancestry in population pharmacogenomics unravels distinct geographical patterns related to drug toxicity

Genetic ancestry plays a major role in pharmacogenomics, and a deeper understanding of the genetic diversity among individuals holds immerse promise for reshaping personalized medicine. In this pivotal study, we have conducted a large-scale genomic analysis of 1,136 pharmacogenomic variants employing machine learning algorithms on 3,714 individuals from publicly available datasets to assess the risk proximity of experiencing drug-related adverse events. Our findings indicate that Admixed Americans and Europeans have demonstrated a higher risk of experiencing drug toxicity, whereas individuals with East Asian ancestry and, to a lesser extent, Oceanians displayed a lower risk proximity. Polygenic risk scores for drug-gene interactions did not necessarily follow similar assumptions, reflecting distinct genetic patterns and population-specific differences that vary depending on the drug class. Overall, our results provide evidence that genetic ancestry is a pivotal factor in population pharmacogenomics and should be further exploited to strengthen even more personalized drug therapy.

Copy-number variants differ in frequency across genetic ancestry groups

Copy-number variants (CNVs) have been implicated in a variety of neuropsychiatric and cognitive phenotypes. We found that deleterious CNVs are less prevalent in non-European ancestry groups than they are in European ancestry groups of both the UK Biobank (UKBB) and a US replication cohort (SPARK). We also identified specific recurrent CNVs that consistently differ in frequency across ancestry groups in both the UKBB and SPARK. These ancestry-related differences in CNV prevalence present in both an unselected community population and a family cohort enriched with individuals diagnosed with autism spectrum disorder (ASD) strongly suggest that genetic ancestry should be considered when probing associations between CNVs and health outcomes.

Insights into pharmacogenetics, drug-gene interactions, and drug-drug-gene interactions

This review explores genetic contributors to drug interactions, known as drug-gene and drug-drug-gene interactions (DGI and DDGI, respectively). This article is part of a mini-review issue led by the International Society for the Study of Xenobiotics (ISSX) New Investigators Group. Pharmacogenetics (PGx) is the study of the impact of genetic variation on pharmacokinetics (PK), pharmacodynamics (PD), and adverse drug reactions. Genetic variation in pharmacogenes, including drug metabolizing enzymes and drug transporters, is common and can increase the risk of adverse drug events or contribute to reduced efficacy. In this review, we summarize clinically actionable genetic variants, and touch on methodologies such as genotyping patient DNA to identify genetic variation in targeted genes, and deep mutational scanning as a high-throughput in vitro approach to study the impact of genetic variation on protein function and/or expression in vitro. We highlight the utility of physiologically based pharmacokinetic (PBPK) models to integrate genetic and chemical inhibitor and inducer data for more accurate human PK simulations. Additionally, we analyze the limitations of historical ethnic descriptors in pharmacogenomics research. Altogether, the work herein underscores the importance of identifying and understanding complex DGI and DDGIs with the intention to provide better treatment outcomes for patients. We also highlight current barriers to wide-scale implementation of PGx-guided dosing as standard or care in clinical settings.

Introduction to the Theme "Pharmacological Individuality: New Insights and Strategies for Personalized and Precise Drug Treatment"

The reviews in Volume 64 of the Annual Review of Pharmacology and Toxicology cover diverse topics. A common theme in many of the reviews is the interindividual variability in the clinical response to drugs. Highlighted areas include emerging developments in pharmacogenomics that can predict the personal risk for drug inefficacy and/or adverse drug reactions. Other reviews focus on the use of circulating biomarkers to define drug metabolism phenotypes and the effect of circadian regulation on drug response. Another emerging technology, digital twins that model individual patients, is used to generate computational simulations of drug effects and identify optimal personalized treatments. Another variable that may affect clinical outcomes, the nocebo response (an adverse reaction to a placebo), complicates clinical trials. These reviews further document that pharmacological individuality is an essential component of the concepts of personalized medicine and precision medicine and will likely have an important impact on patient care.

CYP2D6 and CYP2C19 Variant Coverage of Commercial Antidepressant Pharmacogenomic Testing Panels Available in Victoria, Australia

Pharmacogenomic (PGx) testing to inform antidepressant medication selection and dosing is gaining attention from healthcare professionals, patients, and payors in Australia. However, there is often uncertainty regarding which test is most suitable for a particular patient. Here, we identified and evaluated the coverage of CYP2D6 and CYP2C19 variants in commercial antidepressant PGx testing panels in Victoria, a large and ethnically diverse state of Australia. Test characteristics and star alleles tested for both genes were obtained directly from pathology laboratories offering PGx testing and compared against the Association of Molecular Pathology's recommended minimum (Tier 1) and extended (Tier 2) allele sets. Although all tests covered the minimum recommended alleles for CYP2C19, this was not the case for CYP2D6. This study emphasizes that PGx tests might not be suitable for all individuals in Australia due to the limited range of star alleles assessed. Inadequate haplotype coverage may risk misclassification of an individual's predicted metabolizer phenotype, which has ramifications for depression medication selection and dosage. This study underscores the urgent need for greater standardization in PGx testing and emphasizes the importance of considering genetic ancestry when choosing a PGx testing panel to ensure optimal clinical applicability.