Genetic resources - Preclinical and Clinical
By correlating clinical drug response phenotypes with genetic data, researchers have identified numerous high-risk genetic variants associated with adverse drug reactions and variable treatment outcomes.
Genome-wide association studies (GWAS) have identified numerous genetic variants associated with drug responses and adverse drug reactions (ADRs).
GWAS based on clinical data, while powerful, often identify drug response and ADR associations after patients have already experienced adverse effects.
Preclinical research using human cell models offers an opportunity to proactively identify potential genetic risk factors for a new drug before human exposure.
General
1. Focus on High-Risk Genes in the selected cell models
Genes involved in absorption, distribution, metabolism, and excretion (ADME) processes, particularly Cytochrome P450 genes, are frequently implicated in ADRs. These genes exhibit significant allele frequency differences between populations (see an example: TABLE 1).
The high risk alleles are reported via clinical websites such as PharmGKB (links for this resource and additional ones below).
While the FDA guidelines for “In Vitro Drug Interaction Studies - Cytochrome P450 Enzyme- and Transporter-Mediated Drug Interactions. VIEW” mention CYP genes, ancestry specific references are currently missing.
2. Ordering a Cell Model
Without specific attention to the ancestral background of hepatocyte cell models, you're likely to receive cells of European descent from biorepositories vendors. Consequently, the probability of working with alleles highly prevalent in European populations is increased.
Note: This scenario presents two distinct unknowns:
The ancestry of the cell model. However, the ancestry improves the chance of getting a specific allele, it is not a guarantee.
The relevant alleles present in the model:
Which alleles are present that are relevant?
Are these alleles heterozygous or homozygous in this hepatocyte?
Practical
Know the ancestral origin of cell models used in experiments: Whenever possible, obtain information on the (genetic) ancestral background of cell models used in assays.
(Check the pages: Information, or vendors)Genotype relevant key ADME alleles: After preliminary data suggests an interaction between the drug and ADME associates genes:
Identify high risk alleles (PharmGKB or equal ).
Verify population prevalence variations of those alleles
Match to your ultimate target population (what is the chance specific alleles will be encountered in the diverse clinical trial cohorts).
Verify which alleles your cell models carry (check the Vendor page)
Document genetic information: Record and report the ancestral background and the allelic information for specific alleles used in preclinical studies.
TABLE 1: CYP3A4 metabolizes more than 50% of drugs. CYP3A4 is found in the liver and small intestine. It has 11 reported alleles in PharmGKB. The allele frequency differences between human populations are indicated in the table for four of them. These specific alleles have been associated with alternate responses for a number of (different) drugs.
Comprehensive allele-specific characterization of each gene in experimental cell models is crucial for elucidating the genetic determinants of drug response phenotypes.
For each allele interactions with drug compounds are given and catagorised. This particular allele has interaction with 17 drug compounds with an impact on a number of phenotypes:
Why report both ancestral background AND gene-specific allelic information?
Rationale for Comprehensive Genetic Reporting
Preclinical researchers should report both ancestral background and gene-specific allelic information for cell models and experimental systems. This comprehensive approach is crucial for several reasons:
1.Limited Understanding of Molecular Attributions
Our current knowledge of the molecular effects of specific alleles is incomplete.
This limitation is largely due to the systematic underrepresentation of minority groups in genetic studies over past decades.
2. Known High-Impact Alleles
Several high-impact alleles for specific genes have been identified and are characterized.
Reporting these known alleles is essential for interpreting experimental results and predicting potential drug responses or adverse effects.
3. Potential for Future Discoveries
Numerous alleles contributing to drug-specific phenotypes remain undiscovered.
Reporting ancestral background provides valuable context for future cell GWAS or AI-driven analyses.
Risks of Ignoring Ancestry
(even if ancestry is not your top priority)
Inconsistent ADME Assay Results
Using primary human hepatocytes from different ancestral backgrounds (unknowingly) can lead to inconsistent ADME assay results.
Researchers may mistakenly attribute these differences to experimental error rather than biological variation.