Leveraging human genetics to guide drug target discovery

Multi-level advances in databases related to systems pharmacology in traditional Chinese medicine: a 60-year review

The therapeutic effects of traditional Chinese medicine (TCM) involve intricate interactions among multiple components and targets. Currently, computational approaches play a pivotal role in simulating various pharmacological processes of TCM. The application of network analysis in TCM research has provided an effective means to explain the pharmacological mechanisms underlying the actions of herbs or formulas through the lens of biological network analysis. Along with the advances of network analysis, computational science has coalesced around the core chain of TCM research: formula-herb-component-target-phenotype-ZHENG, facilitating the accumulation and organization of the extensive TCM-related data and the establishment of relevant databases. Nonetheless, recent years have witnessed a tendency toward homogeneity in the development and application of these databases. Advancements in computational technologies, including deep learning and foundation model, have propelled the exploration and modeling of intricate systems into a new phase, potentially heralding a new era. This review aims to delves into the progress made in databases related to six key entities: formula, herb, component, target, phenotype, and ZHENG. Systematically discussions on the commonalities and disparities among various database types were presented. In addition, the review raised the issue of research bottleneck in TCM computational pharmacology and envisions the forthcoming directions of computational research within the realm of TCM.

Rare coding variants in CHRNB2 reduce the likelihood of smoking

Human genetic studies of smoking behavior have been thus far largely limited to common variants. Studying rare coding variants has the potential to identify drug targets. We performed an exome-wide association study of smoking phenotypes in up to 749,459 individuals and discovered a protective association in CHRNB2, encoding the β2 subunit of the α4β2 nicotine acetylcholine receptor. Rare predicted loss-of-function and likely deleterious missense variants in CHRNB2 in aggregate were associated with a 35% decreased odds for smoking heavily (odds ratio (OR) = 0.65, confidence interval (CI) = 0.56-0.76, P = 1.9 × 10^-8). An independent common variant association in the protective direction ( rs2072659 ; OR = 0.96; CI = 0.94-0.98; P = 5.3 × 10^-6) was also evident, suggesting an allelic series. Our findings in humans align with decades-old experimental observations in mice that β2 loss abolishes nicotine-mediated neuronal responses and attenuates nicotine self-administration. Our genetic discovery will inspire future drug designs targeting CHRNB2 in the brain for the treatment of nicotine addiction.

Mendelian Randomization With Repeated Measures of a Time-varying Exposure: An Application of Structural Mean Models

Mendelian randomization (MR) is often used to estimate effects of time-varying exposures on health outcomes using observational data. However, MR studies typically use a single measurement of exposure and apply conventional instrumental variable (IV) methods designed to handle time-fixed exposures. As such, MR effect estimates for time-varying exposures are often biased, and interpretations are unclear. We describe the instrumental conditions required for IV estimation with a time-varying exposure, and the additional conditions required to causally interpret MR estimates as a point effect, a period effect or a lifetime effect depending on whether researchers have measurements at a single or multiple time points. We propose methods to incorporate time-varying exposures in MR analyses based on g-estimation of structural mean models, and demonstrate its application by estimating the period effect of alcohol intake, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol on intermediate coronary heart disease outcomes using data from the Framingham Heart Study. We use this data example to highlight the challenges of interpreting MR estimates as causal effects, and describe other extensions of structural mean models for more complex data scenarios.

Integration of Real-World Data and Genetics to Support Target Identification and Validation

Even modest improvements in the probability of success of selecting drug targets which are ultimately approved can substantially reduce the costs of research and development. Drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. A key enabler of identifying and validating these genetically validated targets is access to association results from genome-wide genotyping, whole-exome sequencing, and whole-genome sequencing studies with observable traits (often diseases) across large numbers of individuals. Today, linkage between genotype and real-world data (RWD) provides significant opportunities to not only increase the statistical power of genome-wide association studies by ascertaining additional cases for diseases of interest, but also to improve diversity and coverage of association studies across the disease phenome. As RWD-genetics linked resources continue to grow in diversity of participants, breadth of data captured, length of observation, and number of participants, there is a greater need to leverage the experience of RWD experts, clinicians, and highly experienced geneticists together to understand which lessons and frameworks from general research using RWD sources are relevant to improve genetics-driven drug discovery and development. This paper describes new challenges and opportunities for phenotypes enabled by diverse RWD sources, considerations in the use of RWD phenotypes for disease gene identification across the disease phenome, and challenges and opportunities in leveraging RWD phenotypes in target validation. The paper concludes with views on the future directions for phenotype development using RWD, and key questions requiring further research and development to advance this nascent field.

Emerging Targets for Cardiovascular Disease Prevention in Diabetes

Type 1 and type 2 diabetes mellitus (T1DM and T2DM) increase the risk of atherosclerotic cardiovascular disease (CVD), resulting in acute cardiovascular events, such as heart attack and stroke. Recent clinical trials point toward new treatment and prevention strategies for cardiovascular complications of T2DM. New antidiabetic agents show unexpected cardioprotective benefits. Moreover, genetic and reverse translational strategies have revealed potential novel targets for CVD prevention in diabetes, including inhibition of apolipoprotein C3 (APOC3). Modeling and pharmacology-based approaches to improve insulin action provide additional potential strategies to combat CVD. The development of new strategies for improved diabetes and lipid control fuels hope for future prevention of CVD associated with diabetes.

Discovery of potential asthma targets based on the clinical efficacy of Traditional Chinese Medicine formulas

ETHNOPHARMACOLOGICAL RELEVANCE

Standard therapy for asthma, a highly heterogeneous disease, is primarily based on bronchodilators and immunosuppressive drugs, which confer short-term symptomatic relief but not a cure. It is difficult to discover novel bronchodilators, although potential new targets are emerging. Traditional Chinese Medicine (TCM) formulas have been used to treat asthma for more than 2000 years, forming the basis for representative asthma treatments.

AIM OF THE STUDY

Based on the efficacy of TCM formulas, anti-asthmatic herbal compounds bind proteins are potential targets for asthma therapy. This analysis will provide new drug targets and discovery strategies for asthma therapy.

MATERIALS AND METHODS

A list of candidate herbs for asthma was selected from the classical formulas (CFs) of TCM for the treatment of wheezing or dyspnea recorded in Treatise on Cold Damage and Miscellaneous Diseases (TCDMD) and from modern herbal formulas identified in the SAPHRON TCM Database using the keywords "wheezing" or "dyspnea". Compounds in the selected herbs and compounds that directly bind target proteins were acquired by searching the Herbal Ingredients' Targets Database (HITD), TCM Data Bank (TCMDB) and TCM Integrated Database (TCMID). Therapeutic targets of conventional medicine (CM) for asthma were collected by searching Therapeutic Target Database (TTD), DrugBank and PubMed as supplements. Finally, the enriched gene ontology (GO) terms of the targets were obtained using the Database for Annotation Visualization and Integrated Discovery (DAVID) and protein-protein interactions (PPI) networks were constructed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). The effects of two selected TCM compounds, kaempferol and ginkgolide A, on cellular resistance in human airway smooth muscle cells (ASMCs) and pulmonary resistance in a mouse model were investigated.

RESULTS

The list of 32 candidate herbs for asthma was selected from 10 CFs for the treatment of wheezing or dyspnea recorded in TCDMD and 1037 modern herbal formulas obtained from the SAPHRON TCM Database. A total of 130 compounds from the 32 selected herbs and 68 herbal compounds directly bind target proteins were acquired from HITD and TCMDB. Eighty-eight therapeutic targets of CM for asthma were collected by searching TTD and PubMed as supplements. DAVID and STRING analyses showed targets of TCM formulas are primarily related to cytochrome P450 (CYP) family, transient receptor potential (TRP) channels, matrix metalloproteinases (MMPs) and ribosomal protein. Both TCM formulas and CM act on the same types of targets or signaling pathways, such as G protein-coupled receptors (GPCRs), steroid hormone receptors (SHRs), and JAK-STAT signaling pathway. The proteins directly targeted by herbal compounds, TRPM8, TRPA1, TRPV3, CYP1B1, CYP2B6, CYP1A2, CYP3A4, CYP1A1, PPARA, PPARD, NR1I2, MMP1, MMP2, ESR1, ESR2, RPLP0, RPLP1 and RPLP2, are potential targets for asthma therapy. In vitro results showed kaempferol (1 × 10^-2 mM) and ginkgolide A (1 × 10^-5 mM) significantly increased the cell index (P < 0.05 vs. histamine, n = 3) and therefore relaxed human ASMCs. In vivo results showed kaempferol (145 μg/kg) and ginkgolide A (205 μg/kg) significantly reduced pulmonary resistance (P < 0.05 vs. methacholine, n = 6).

CONCLUSION

Potential target discovery for asthma treatment based on the clinical effectiveness of TCM is a feasible strategy.

Cholesterol-Lowering Agents

Loss-of-function variants in PCSK9 (proprotein convertase subtilisin-kexin type 9) are associated with lower lifetime risk of atherosclerotic cardiovascular disease) events. Confirmation of these genetic observations in large, prospective clinical trials in participants with atherosclerotic cardiovascular disease has provided guidance on risk stratification and enhanced our knowledge on hitherto unresolved and contentious issues concerning the efficacy and safety of markedly lowering LDL-C (low-density lipoprotein cholesterol). PCSK9 has a broad repertoire of molecular effects. Furthermore, clinical trials with PCSK9 inhibitors demonstrate that reductions in atherosclerotic cardiovascular disease events are more effective in patients with recent myocardial infarction, multiple myocardial infarctions, multivessel coronary artery disease, and lower extremity arterial disease. The potent LDL-C lowering efficacy of PCSK9 inhibitors provides the opportunity for more aggressive LDL-lowering strategies in high-risk patients with atherosclerotic cardiovascular disease and supports the notion that there is no lower limit for LDL-C. Aggressive LDL-C lowering with fully human PCSK9 monoclonal antibodies has been associated by a safety profile superior to that of other classes of LDL-lowering agents. These clinical trials provide evidence that LDL lowering with PCSK9 inhibitors is an effective therapy for lowering cardiovascular events in high-risk patients with LDL-C levels ≥70 mg/dL on maximally tolerated oral therapies, including statins and ezetimibe.

Lipid-Lowering Agents

Several new or emerging drugs for dyslipidemia owe their existence, in part, to human genetic evidence, such as observations in families with rare genetic disorders or in Mendelian randomization studies. Much effort has been directed to agents that reduce LDL (low-density lipoprotein) cholesterol, triglyceride, and Lp[a] (lipoprotein[a]), with some sustained programs on agents to raise HDL (high-density lipoprotein) cholesterol. Lomitapide, mipomersen, AAV8.TBG.hLDLR, inclisiran, bempedoic acid, and gemcabene primarily target LDL cholesterol. Alipogene tiparvovec, pradigastat, and volanesorsen primarily target elevated triglycerides, whereas evinacumab and IONIS-ANGPTL3-L_Rx target both LDL cholesterol and triglyceride. IONIS-APO(a)-L_Rx targets Lp(a).

A decade of genome-wide association studies for coronary artery disease: the challenges ahead

In this review, we summarize current knowledge on the genetics of coronary artery disease, based on 10 years of genome-wide association studies. The discoveries began with individual studies using 200K single nucleotide polymorphism arrays and progressed to large-scale collaborative efforts, involving more than a 100 000 people and up to 40 Mio genetic variants. We discuss the challenges ahead, including those involved in identifying causal genes and deciphering the links between risk variants and disease pathology. We also describe novel insights into disease biology based on the findings of genome-wide association studies. Moreover, we discuss the potential for discovery of novel treatment targets through the integration of different layers of 'omics' data and the application of systems genetics approaches. Finally, we provide a brief outlook on the potential for precision medicine to be enhanced by genome-wide association study findings in the cardiovascular field.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Implications of de novo mutations in guiding drug discovery: A study of four neuropsychiatric disorders

Recent studies have suggested an important role of de novo mutations (DNMs) in neuropsychiatric disorders. As DNMs are not subject to elimination due to evolutionary pressure, they are likely to have greater disruptions on biological functions. While a number of sequencing studies have been performed on neuropsychiatric disorders, the implications of DNMs for drug discovery remain to be explored. In this study, we employed a gene-set analysis approach to address this issue. Four neuropsychiatric disorders were studied, including schizophrenia (SCZ), autistic spectrum disorders (ASD), intellectual disability (ID) and epilepsy. We first identified gene-sets associated with different drugs, and analyzed whether the gene-set pertaining to each drug overlaps with DNMs more than expected by chance. We also assessed which medication classes are enriched among the prioritized drugs. We discovered that neuropsychiatric drug classes were indeed significantly enriched for DNMs of all four disorders; in particular, antipsychotics and antiepileptics were the most strongly enriched drug classes for SCZ and epilepsy respectively. Interestingly, we revealed enrichment of several unexpected drug classes, such as lipid-lowering agents for SCZ and anti-neoplastic agents. By inspecting individual hits, we also uncovered other interesting drug candidates or mechanisms (e.g. histone deacetylase inhibition and retinoid signaling) that might warrant further investigations. Taken together, this study provided evidence for the usefulness of DNMs in guiding drug discovery or repositioning.

Shortcut to success? Negotiating genetic uniqueness in global biomedicine

Since the sequencing of the human genome, as well as the completion of the first Human Genome Diversity Project, the benefits of studying one human population over another has been an ongoing debate relating to the replicability of findings in other populations. The leveraging of specific populations into research markets has made headlines in cases such as deCode in Iceland, Quebec Founder Population, and Generation Scotland. In such cases, researchers and policy makers have used the genetic and historical uniqueness of their populations to attract scientific, commercial and political interest. In this article, we explore how in countries with population isolates, such as Finland, the researchers balance considerations relating to the generalization and replicability of findings in small yet unique research populations to global biomedical research interests. This highlights challenges related to forms of competition associated with genetics research markets, as well as what counts as the 'right' population for genetic research.

Mendelian randomization: a novel approach for the prediction of adverse drug events and drug repurposing opportunities

Identification of unintended drug effects, specifically drug repurposing opportunities and adverse drug events, maximizes the benefit of a drug and protects the health of patients. However, current observational research methods are subject to several biases. These include confounding by indication, reverse causality and missing data. We propose that Mendelian randomization (MR) offers a novel approach for the prediction of unintended drug effects. In particular, we advocate the synthesis of evidence from this method and other approaches, in the spirit of triangulation, to improve causal inferences concerning drug effects. MR addresses some of the limitations associated with the existing methods in this field. Furthermore, it can be applied either before or after approval of the drug, and could therefore prevent the potentially harmful exposure of patients in clinical trials and beyond. The potential of MR as a pharmacovigilance and drug repurposing tool is yet to be realized, and could both help prevent adverse drug events and identify novel indications for existing drugs in the future.

Next-generation sequencing in drug development: target identification and genetically stratified clinical trials

Next-generation sequencing (NGS) enabled high-throughput analysis of genotype-phenotype relationships on human populations, ushering in a new era of genetics-informed drug development. The year 2017 was remarkable, with the first FDA-approved gene therapy for cancer (Kymriah™) and for inherited diseases (LUXTURNA™), the first multiplex NGS panel for companion diagnostics (MSK-IMPACT™) and the first drug targeting a genetic signature rather than a disease (Keytruda^®). We envision that population-scale NGS with paired electronic health records (EHRs) will become a routine measure in the drug development process for the identification of novel drug targets, and that genetically stratified clinical trials could be widely adopted to improve power in precision-medicine-guided drug development.

Therapeutic oligonucleotides in cardiovascular and metabolic diseases: insights for the internist

The idea of using small RNA fragments (oligonucleotides) for therapeutic purposes dates back to the 1990s, following the landmark discoveries on the mechanisms of gene silencing and RNA-interference (RNA-i). However, the first applications in medicine were hampered by difficulties in chemical stabilization and efficient delivery to target tissues. Recent advances in chemical manipulation of oligonucleotides have, at least partially, bypassed such obstacles. In particular, conjugation with ligands for specific receptors allows the selective uptake of oligonucleotides by critical cells (e.g., hepatocytes), where they inhibit the synthesis of the target protein by binding the complementary mRNA and inducing its degradation. In parallel, next-generation sequencing (NGS) studies at population levels have identified a number of key molecular targets, mainly through the discovery of "human knock-outs," i.e., subjects lacking a given protein because of nonsense mutations in the corresponding gene. Such highly informative individuals are often healthy, or even protected from the development of certain diseases. Indeed, subjects with null mutations in certain genes controlling lipoprotein metabolism like PCSK9 or ANGPTL-3 have a lower risk of cardiovascular disease. Since the complete absence of such proteins does not appear to carry any negative health effect, the corresponding genes are ideal candidates for the silencing approach. Pilot clinical trials with long acting anti-PCSK9 or anti-ANGPTL-3 oligonucleotides have yielded very promising results, so that their use as "vaccines" against atherosclerosis has been suggested in the future. As therapeutic oligonucleotides can virtually target innumerable proteins, their increasing development is predicted to substantially expand the repertoire of the "biological drugs," in addition to, or even substituting, more consolidated approaches like monoclonal antibodies.

The genetics of obstructive sleep apnoea

Obstructive sleep apnoea (OSA) is a common chronic disease and is associated with high social and economic costs. OSA is heritable, and there is evidence of both direct genetic contributions to OSA susceptibility and indirect contributions via 'intermediate' phenotypes such as obesity, craniofacial structure, neurological control of upper airway muscles and of sleep and circadian rhythm. Investigation of the genetics of OSA is an important research area and may lead to improved understanding of disease aetiology, pathogenesis, adverse health consequences and new preventive strategies and treatments. Genetic studies of OSA have lagged behind other chronic diseases; however recent gene discovery efforts have been successful in finding genetic loci contributing to OSA-associated intermediate phenotypes. Nevertheless, many of the seminal questions relating to the genetic epidemiology of OSA and associated factors remain unanswered. This paper reviews the current state of knowledge of the genetics of OSA, with a focus on genomic approaches to understanding sleep apnoea.

Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders

High and increasing prevalence of neurodevelopmental disorders place enormous personal and economic burdens on society. Given the growing realization that the roots of neurodevelopmental disorders often lie in early childhood, there is an urgent need to identify childhood risk factors. Neurodevelopment is marked by periods of heightened experience-dependent neuroplasticity wherein neural circuitry is optimized by the environment. If these critical periods are disrupted, development of normal brain function can be permanently altered, leading to neurodevelopmental disorders. Here, we aim to systematically identify human variants in neuroplasticity-related genes that confer risk for neurodevelopmental disorders. Historically, this knowledge has been limited by a lack of techniques to identify genes related to neurodevelopmental plasticity in a high-throughput manner and a lack of methods to systematically identify mutations in these genes that confer risk for neurodevelopmental disorders. Using an integrative genomics approach, we determined loss-of-function (LOF) variants in putative plasticity genes, identified from transcriptional profiles of brain from mice with elevated plasticity, that were associated with neurodevelopmental disorders. From five shared differentially expressed genes found in two mouse models of juvenile-like elevated plasticity (juvenile wild-type or adult Lynx1-/- relative to adult wild-type) that were also genotyped in the Mount Sinai BioMe Biobank we identified multiple associations between LOF genes and increased risk for neurodevelopmental disorders across 10,510 patients linked to the Mount Sinai Electronic Medical Records (EMR), including epilepsy and schizophrenia. This work demonstrates a novel approach to identify neurodevelopmental risk genes and points toward a promising avenue to discover new drug targets to address the unmet therapeutic needs of neurodevelopmental disease.

A decade of progress on the genetic basis of coronary artery disease. Practical insights for the internist

Clinicians are well aware of the importance of a positive family history for coronary artery disease (CAD). Nonetheless, elucidation of the genetic basis of CAD has long proven difficult. The scenario changed in the last decade through the application of modern genomic technologies, like genome-wide association studies (GWAS) and next generation sequencing (NGS). GWAS have discovered over 60 common variants highly associated with CAD. For predictive purposes, such variants have been used to build up Genetic Risk Scores (GRSs), but their incorporation into classical prediction models does not appear substantially outperform the simple addition of family history. To date, the only strong case for the utility of incorporating genetic testing into clinical practice is represented by the diagnosis of Familial Hypercholesterolemia (FH). On the other hand, utilization of genomic techniques has driven formidable advances into the knowledge of CAD pathophysiology, particularly by addressing controversies on the causality of some lipid fractions that had long remained unsolved because of limitations of observational epidemiology. For example, NGS-derived rare variants with strong functional effects on key-genes like ANGPTL4, APOA5, APOC3, LPL, and SCARB1, have proven useful as proxies to demonstrate the causality of triglyceride-rich lipoproteins (TRLs) at variance with HDL-cholesterol concentration, thus contributing to tear down a dogma from classical epidemiology. Moreover, such variants have paved the way for the development of new biologic drugs (i.e. monoclonal antibodies or antisense oligonucleotides) targeting key proteins like PCSK9, Lipoprotein_(a), and apolipoprotein C3. Such drugs are currently under active investigation, with first results being extremely promising.

ANGPTL3 Deficiency and Protection Against Coronary Artery Disease

BACKGROUND

Familial combined hypolipidemia, a Mendelian condition characterized by substantial reductions in all 3 major lipid fractions, is caused by mutations that inactivate the gene angiopoietin-like 3 (ANGPTL3). Whether ANGPTL3 deficiency reduces risk of coronary artery disease (CAD) is unknown.

OBJECTIVES

The study goal was to leverage 3 distinct lines of evidence-a family that included individuals with complete (compound heterozygote) ANGPTL3 deficiency, a population based-study of humans with partial (heterozygote) ANGPTL3 deficiency, and biomarker levels in patients with myocardial infarction (MI)-to test whether ANGPTL3 deficiency is associated with lower risk for CAD.

METHODS

We assessed coronary atherosclerotic burden in 3 individuals with complete ANGPTL3 deficiency and 3 wild-type first-degree relatives using computed tomography angiography. In the population, ANGPTL3 loss-of-function (LOF) mutations were ascertained in up to 21,980 people with CAD and 158,200 control subjects. LOF mutations were defined as nonsense, frameshift, and splice-site variants, along with missense variants resulting in <25% of wild-type ANGPTL3 activity in a mouse model. In a biomarker study, circulating ANGPTL3 concentration was measured in 1,493 people who presented with MI and 3,232 control subjects.

RESULTS

The 3 individuals with complete ANGPTL3 deficiency showed no evidence of coronary atherosclerotic plaque. ANGPTL3 gene sequencing demonstrated that approximately 1 in 309 people was a heterozygous carrier for an LOF mutation. Compared with those without mutation, heterozygous carriers of ANGPTL3 LOF mutations demonstrated a 17% reduction in circulating triglycerides and a 12% reduction in low-density lipoprotein cholesterol. Carrier status was associated with a 34% reduction in odds of CAD (odds ratio: 0.66; 95% confidence interval: 0.44 to 0.98; p = 0.04). Individuals in the lowest tertile of circulating ANGPTL3 concentrations, compared with the highest, had reduced odds of MI (adjusted odds ratio: 0.65; 95% confidence interval: 0.55 to 0.77; p < 0.001).

CONCLUSIONS

ANGPTL3 deficiency is associated with protection from CAD.