Drug Development Genomics
Overview
In the dynamic environment of drug development, understanding the genetic basis of a population is crucial for creating more effective and safer drugs. Population genetics offers valuable insights that can revolutionize the entire drug development pipeline, from initial discovery to post-market monitoring. The responses of different groups of people to the same drug may vary significantly, and such differences are often closely related to genetic variations. Population genetics services analyze the genetic data of large-scale populations to identify key genetic variations related to drug responses, providing important target information for drug development. Our platform integrates cutting-edge multi-omics data with AI-driven pharmacogenomics models, dedicated to providing powerful R&D tools for the pharmaceutical and biotechnology industries. Its core objective is to analyze the genetic factors that lead to individual differences in drug responses, thereby providing crucial data insights and decision support during the drug discovery and development stages.
Our Population Genetics Services for Drug Development Enhance Your Research with:
- Optimized Drug Metabolism Profiling: Our services accurately determine whether an individual is a fast, normal, or slow metabolizer. This information is crucial for tailoring drug dosages and selecting the most appropriate medications, ensuring optimal therapeutic outcomes while minimizing the risk of adverse reactions.
- Improving the efficiency of drug development: Population genetics services help optimize drug design, reduce unnecessary clinical trials and lower drug development costs by providing precise genetic information. For instance, through population genetics research, the metabolic capacity and efficacy of specific drugs in different populations can be predicted, thereby screening out the most promising drug candidates in the early stage of drug development and enhancing the efficiency of drug development.
- Enhancing drug safety: Population genetics services help identify high-risk groups in the early stage of drug development by predicting the safety risks of specific drugs in different populations, thereby reducing the incidence of adverse drug reactions. In addition, population genetics services can also provide scientific basis for drug safety assessment and offer strong support for the decision-making of drug regulatory agencies.
What Is Drug Development: Population Genetics Services
Drug development genomics makes use of population genetics and multi-omics integration. It aims to figure out how genetic variations affect disease mechanisms, drug responses, and treatment results. By analyzing transcriptomic, epigenomic, and proteomic data alongside genomic variants, researchers gain a holistic understanding of molecular pathways disrupted by SVs, enabling precision medicine strategies tailored to diverse populations.
How We Deliver This Solution
Sequencing
Whole Genome Sequencing (WGS)
- Purpose: Comprehensive detection of single-nucleotide variants (SNVs), structural variants (SVs), and copy number alterations (CNAs) across populations.
- Application: Identifies rare disease-causing mutations, population-specific genetic markers, and drug-response biomarkers.
- Link: /pop-genomics/seq/whole-genome-reseq.html
Whole Exome Sequencing (WES)
- Purpose: High-throughput sequencing of protein-coding regions to prioritize functional variants.
- Application: Accelerates discovery of disease-associated genes and therapeutic targets in large cohorts.
- Link: /pop-genomics/seq/whole-exome-sequencing.html
Genotyping by Sequencing (GBS)
- Purpose: Cost-effective, high-density genotyping across thousands of samples using reduced-representation libraries.
- Application: Enables large-scale association studies (GWAS) and population stratification for clinical trials.
- Link: /pop-genomics/seq/gbs.html
Spatial Transcriptomics
- Purpose: Maps gene expression within tissue architecture to identify spatial interactions between cell types.
- Application: Reveals microenvironmental contributions to drug resistance and tumor metastasis.
CRISPR Screening & Functional Genomics
- Purpose: High-throughput loss-of-function screens to identify gene networks regulating drug response or disease phenotypes.
- Application: Validates novel therapeutic targets and predicts synthetic lethality in cancer.
Bioinformatics
Population Dynamics Analysis
- Purpose: Tracks genetic shifts across time, geography, and environmental pressures.
- Application: Predicts drug resistance evolution in pathogens (e.g., antibiotic-resistant bacteria).
- Link: /pop-genomics/bioinfo/population-dynamics-analysis.html
Population Structure Analysis
- Purpose: Deconstructs ancestry and admixture patterns to identify stratified risk alleles.
- Application: Guides inclusive clinical trial design for global populations.
- Link: /pop-genomics/bioinfo/population-structure-analysis.html
PCA Analysis Service
- Purpose: Visualizes genetic clustering to detect population outliers or confounders.
- Application: Corrects for population stratification in GWAS and pharmacogenomic studies.
- Link: /pop-genomics/bioinfo/pca-analysis-service.html
Evolutionary Tree Analysis
- Purpose: Reconstructs phylogenetic relationships to trace pathogen origins or drug resistance mutations.
- Application: Monitors viral evolution (e.g., SARS-CoV-2 variants) to update vaccine targets.
- Link: /pop-genomics/bioinfo/evolutionary-tree-analysis.html
Service flowchart
Figure 1: Drug Development: Population Genetics Services
Our Advantages
- Scalable Cohort Design & Large-Scale Population Genomics
Support projects with 10,000+ samples, enabling robust statistical power for rare variant discovery and stratified subgroup analyses.
Expertise in multi-ethnic and global cohort integration, ensuring findings are generalizable across diverse populations (e.g., GWAS with >500,000 participants).
- End-to-End Integration: Sequencing + Bioinformatics + AI-Driven Insights
Unified pipeline combining WGS/WES/GBS with population structure analysis, PCA, and evolutionary tree modeling to prioritize functionally relevant variants.
AI-powered tools (e.g., deep learning for non-coding region annotation) enhance target prediction accuracy, reducing off-target risks in CRISPR/drug screens.
- Cost-Effective, High-Throughput Genotyping Solutions
Genotyping-by-sequencing (GBS) reduces costs by 50–70% vs. whole-genome arrays, while maintaining high density (>1M SNPs) for GWAS and pharmacogenomic studies.
Cloud-based automation streamlines data processing, enabling rapid turnaround (e.g., 4–6 weeks for WGS analysis at scale).
- Publication-Ready Evidence & Regulatory-Grade Reporting
Deliver pre-validated datasets aligned with FDA/EMA guidelines, including variant effect predictions (ClinVar, ACMG) and population frequency annotations (gnomAD, 1000 Genomes).
Proven track record in high-impact journals (Nature, Cell, NEJM), with 90% of client projects published within 18 months of completion.
Applications
1. Target Identification and Validation
Genomic profiling uncovers disease-associated genes and pathways, providing high-confidence targets for drug development.
Example: Cystic Fibrosis (CF) and CFTR Modulators
- Challenge: CF is caused by mutations in the CFTR gene, leading to defective chloride channels. Traditional therapies treated symptoms, not the root cause.
- Genomic Solution:
- High-throughput sequencing identified thousands of CFTR mutations, classifying them by functional impact (e.g., splicing defects, protein misfolding).
- This enabled the development of CFTR modulators (e.g., ivacaftor, lumacaftor), which correct specific mutation classes at the molecular level.
- Outcome:
- Ivacaftor improved lung function by ~10% in patients with the G551D mutation, transforming CF from a fatal disease to a manageable condition.
- Genomic-guided targeting reduced development timelines by focusing on mutations with clear biological relevance.
2. Drug Repurposing via Genomic Associations
Genome-wide association studies (GWAS) identify links between genes and diseases, uncovering new uses for existing drugs.
Example: Rheumatoid Arthritis (RA) and TNF-α Inhibitors
- Challenge: RA is a chronic autoimmune disease with limited targeted therapies.
- Genomic Solution:
- GWAS linked RA to variants in the TNF gene region, encoding the pro-inflammatory cytokine TNF-α.
- This validated TNF-α as a therapeutic target, leading to repurposing of anti-TNF biologics (e.g., infliximab, adalimumab), originally developed for Crohn's disease.
- Outcome:
- Anti-TNF therapy reduced RA disease activity by ~50% in clinical trials, becoming a cornerstone of RA management.
- Genomic insights accelerated approval by leveraging existing safety data from other indications.
3. Predicting Adverse Drug Reactions (ADRs)
Pharmacogenomic testing identifies genetic variants that influence drug metabolism, minimizing toxicity risks.
Example: Abacavir and HLA-B*57:01 Screening
- Challenge: Abacavir, an HIV antiretroviral, causes life-threatening hypersensitivity reactions (HSR) in ~5% of patients.
- Genomic Solution:
- GWAS found that carriers of the HLA-B57:01* allele had a 100% risk of abacavir-induced HSR.
- Routine HLA-B57:01 testing now prevents HSR by excluding at-risk patients.
- Outcome:
- HSR rates dropped from 5% to <0.1% post-implementation, improving patient safety and adherence.
- This exemplifies how genomics can de-risk drug development by preemptively identifying high-risk groups.
Demo
Figure 2: Unveiling Japanese Genetic Substructure and Its Impact on Polygenic Risk Prediction: A Multidimensional Genomic Analysis. (Sakaue, 2020)
Case Study
Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.
Journal：Sci Adv.
Published：2024
Whole - genome sequencing (WGS) datasets are now essential for pushing forward human genetic research. They allow scientists to study genome variations, understand population history, and explore evolutionary adaptation.
However, most large - scale WGS projects mainly focus on people of European descent. For instance, well - known projects such as the U.K. Biobank, FinnGen, and deCODE mainly sample European individuals. This focus on Europe has caused problems in precision medicine. Polygenic risk scores, which are used to predict disease risks, are much more accurate for Europeans than for people from other ethnic groups.
These biases are a big issue. They make it hard to apply genetic discoveries widely and worsen health inequalities. That's because interpreting genetic variants, doing imputation analysis, and identifying drug targets all need diverse reference data.
Although there have been attempts to sequence East Asian populations, like GenomeAsia 100K and ChinaMap, Japanese genomic resources were scattered until recently. Studies like ToMMo only gave a regional view. Without a nationwide, clinically - labeled Japanese WGS dataset, it was difficult to fully study the unique genetic makeup and disease - related variants of the Japanese population.
To overcome these limitations, the Japanese Encyclopedia of Whole-Genome/Exome Sequencing Library (JEWEL) was developed using samples from Biobank Japan (BBJ), a nationwide patient-based biobank spanning diverse geographic regions. Unlike ToMMo, which focused on the northeastern Japanese population, JEWEL aimed to capture the genetic heterogeneity of mainland and Ryukyu clusters through principal components analysis (PCA) and fine-scale population structure analysis The project integrated deep phenotyping from medical records, follow-up surveys, and clinical examinations, enriching the dataset with disease-associated variants and longitudinal health data. By leveraging BBJ's infrastructure, JEWEL provided a high-resolution catalog of common and rare variants, loss-of-function (LoF) mutations, and archaic introgression segments from Neanderthals or Denisovans. Additionally, the dataset supported imputation analysis for genome-wide association studies (GWAS) and enabled targeted examination of carriers with pathogenic variants, addressing gaps in variant interpretation for genetic counseling.
JEWEL revealed critical insights into Japanese genetic architecture, including a dual population structure (mainland and Ryukyu clusters) and substantial heterogeneity within mainland subgroups. The dataset identified rare variants, structural variants, and LoF mutations linked to diseases, offering opportunities to explore human knockout effects and therapeutic targets. Notably, JEWEL characterized archaic introgression segments, shedding light on adaptive evolution in East Asia. Furthermore, the integration of deep phenotyping permitted associations between genetic variants and clinical outcomes, enhancing the precision of polygenic risk models. By providing summary-level allele frequencies (AFs) and a continually updated reference panel, JEWEL advanced equitable genomic medicine, enabling variant interpretation tailored to the Japanese population. These findings underscored the value of diverse, clinically annotated WGS datasets in driving personalized medicine and reducing health disparities globally.
Figure 3: Two-dimensional illustrations of biobank-scale genotype data from the Japanese population by the five dimensionality reduction methods.
FAQ
A: Traditional screening often focuses on common variants in European populations, while population genetics services:
- Analyze ancestry-specific rare variants (e.g., HBB mutations causing sickle cell in African populations).
- Use global reference panels (e.g., 1000 Genomes Project) to avoid bias.
- Integrate epidemiological data (e.g., diabetes prevalence in Polynesians) to prioritize targets.
A:
- FDA: Requires ancestry-stratified efficacy data in approvals (e.g., 2022 guidance on heart failure drugs).
- EMA: Launched the Genomic Medicine Strategy to standardize PGx testing across Europe.
- Global Initiatives: The WHO's Human Genome Diversity Project aims to close data gaps.
References
- Sakaue S, Hirata J, Kanai M, et al. Dimensionality reduction reveals fine-scale structure in the Japanese population with consequences for polygenic risk prediction. Nat Commun. 2020 Mar 26;11(1):1569. https://doi.org/10.1038/s41467-020-15194-z
- Liu X, Koyama S, Tomizuka K, et al. Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing. Sci Adv. 2024 Apr 19;10(16):eadi8419. https://doi.org/10.1126/sciadv.adi8419
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