HLA-associated drug hypersensitivity and the prediction of adverse drug reactions

Adverse drug reactions are an important cause of morbidity and mortality and constitute the leading reason of drug withdrawal from the market. Besides classical reactions that are related to pharmacologic activity of the drug, some reactions are unpredictable, not dose dependent, and seem to occur in genetically predisposed individuals. The majority of this reaction is immunologically driven and they are referred to as hypersensitivity reactions. A growing number of studies provided evidences that specific HLA alleles increase the risk of developing hypersensitivity drug reactions. In this context, drug hypersensitivities that have more robust pharmacogenetic data include abacavir hypersensitivity syndrome and severe cutaneous adverse reactions induced by allopurinol and carbamazepine

Clustering and Alignment of Polymorphic Sequences for HLA-DRB1 Genotyping

Located on Chromosome 6p21, classical human leukocyte antigen genes are highly polymorphic. HLA alleles associate with a variety of phenotypes, such as narcolepsy, autoimmunity, as well as immunologic response to infectious disease. Moreover, high resolution genotyping of these loci is critical to achieving long-term survival of allogeneic transplants. Development of methods to obtain high resolution analysis of HLA genotypes will lead to improved understanding of how select alleles contribute to human health and disease risk. Genomic DNAs were obtained from a cohort of n = 383 subjects recruited as part of an Ulcerative Colitis study and analyzed for HLA-DRB1. HLA genotypes were determined using sequence specific oligonucleotide probes and by next-generation sequencing using the Roche/454 GSFLX instrument. The Clustering and Alignment of Polymorphic Sequences (CAPSeq) software application was developed to analyze next-generation sequencing data. The application generates HLA sequence specific 6-digit genotype information from next-generation sequencing data using MUMmer to align sequences and the R package diffusionMap to classify sequences into their respective allelic groups. The incorporation of Bootstrap Aggregating, Bagging to aid in sorting of sequences into allele classes resulted in improved genotyping accuracy. Using Bagging iterations equal to 60, the genotyping results obtained using CAPSeq when compared with sequence specific oligonucleotide probe characterized 4-digit genotypes exhibited high rates of concordance, matching at 759 out of 766 (99.1%) alleles. © 2013 Ringquist et al

AAPS Workshop Report: Strategies to Address Therapeutic Protein–Drug Interactions during Clinical Development

Therapeutic proteins (TPs) are increasingly combined with small molecules and/or with other TPs. However preclinical tools and in vitro test systems for assessing drug interaction potential of TPs such as monoclonal antibodies, cytokines and cytokine modulators are limited. Published data suggests that clinically relevant TP-drug interactions (TP-DI) are likely from overlap in mechanisms of action, alteration in target and/or drug-disease interaction. Clinical drug interaction studies are not routinely conducted for TPs because of the logistical constraints in study design to address pharmacokinetic (PK)- and pharmacodynamic (PD)-based interactions. Different pharmaceutical companies have developed their respective question- and/or risk-based approaches for TP-DI based on the TP mechanism of action as well as patient population. During the workshop both company strategies and regulatory perspectives were discussed in depth using case studies; knowledge gaps and best practices were subsequently identified and discussed. Understanding the functional role of target, target expression and their downstream consequences were identified as important for assessing the potential for a TP-DI. Therefore, a question-and/or risk-based approach based upon the mechanism of action and patient population was proposed as a reasonable TP-DI strategy. This field continues to evolve as companies generate additional preclinical and clinical data to improve their understanding of possible mechanisms for drug interactions. Regulatory agencies are in the process of updating their recommendations to sponsors regarding the conduct of in vitro and in vivo interaction studies for new drug applications (NDAs) and biologics license applications (BLAs)