Indiana University-Purdue University Indianapolis (IUPUI) / The antiretroviral drug efavirenz remains a cornerstone for treatment-naïve HIV patients. Subsequent to the demonstration that efavirenz is a substrate of cytochrome P450 (CYP) 2B6, a number of clinical studies found that the CYP2B6*6 allele is significantly associated with higher efavirenz exposure and/or adverse reactions. However, the mechanism of reduced efavirenz metabolism by this genetic variant is not fully understood and whether this variant exhibits differential susceptibility to metabolic inhibition is also unknown. Ths use of efavirenz is further complicated by the drug interactions associated with it. Therefore, I hypothezised that 1) the CYP2B6*6 allele reduces efavirenz metabolism by altering catalytic properties of CYP2B6; 2) efavirenz alters the pharmacokinetics of co-administered drugs by inhibiting drug metabolizing enzymes. A series of studies was carried out in hepatic microsomal preparations to determine the functional consequences of the CYP2B6*6 allele and to assess inhibition potency of efavirenz on 8 CYPs. The major findings for these studies include: 1) the CYP2B6*6 allele reduces efavirenz metabolism by decreasing substrate binding and catalytic efficiency; 2) functional consequences of the CYP2B6*6 allele appear to be substrate- and cytochrome b5-dependent; 3) the CYP2B6*6 allele confers increased susceptibility to metabolic inhibition; and 4) efavirenz inhibits the activities of CYP2B6, 2C8, 2C9 and 2C19 at therapeutically relevant concentrations. In addition, I explored the hypothesis that the incorporation of in vitro mechanism by which the CYP2B6*6 allele reduced efavirenz metabolism predicts the genetic effect of this allele on efavirenz clearance after a single oral dose by modeling approach. A pharmacogenetics-based in vitro-in vivo extrapolation (IVIVE) model was developed to predict human efavirenz clearance. Taken together, results from this dissertation provide new mechanistic information on how the CYP2B6*6 allale alters substrate metabolism and drug interactions; demonstrate new mechanisms of efavirenz-mediated inhibition interactions; and demonstrate the utility of a pharmacogenetics-based predictive model that can serve as a basis for future studies with efavirenz and other CYP2B6 substrates. Overall these data provide improved understanding of genetic and non-genetic determinant of efavirenz disposition and drug interactions associated with it.

Indiana University-Purdue University Indianapolis (IUPUI) / Drug-drug interactions (DDIs) refer to situations where one drug affects the pharmacokinetics or pharmacodynamics of another. DDIs represent a major cause of morbidity and mortality. A common adverse drug reaction (ADR) that can result from, or be exacerbated by DDIs is drug-induced myopathy. Identifying DDIs and understanding their underlying mechanisms is key to the prevention of undesirable effects of DDIs and to efforts to optimize therapeutic outcomes. This dissertation is dedicated to identification of clinically important myopathic DDIs and to elucidation of their underlying mechanisms. Using data mined from the published cytochrome P450 (CYP) drug interaction literature, 13,197 drug pairs were predicted to potentially interact by pairing a substrate and an inhibitor of a major CYP isoform in humans. Prescribing data for these drug pairs and their associations with myopathy were then examined in a large electronic medical record database. The analyses identified fifteen drug pairs as DDIs significantly associated with an increased risk of myopathy. These significant myopathic DDIs involved clinically important drugs including alprazolam, chloroquine, duloxetine, hydroxychloroquine, loratadine, omeprazole, promethazine, quetiapine, risperidone, ropinirole, trazodone and simvastatin. Data from in vitro experiments indicated that the interaction between quetiapine and chloroquine (risk ratio, RR, 2.17, p-value 5.29E-05) may result from the inhibitory effects of quetiapine on chloroquine metabolism by cytochrome P450s (CYPs). The in vitro data also suggested that the interaction between simvastatin and loratadine (RR 1.6, p-value 4.75E-07) may result from synergistic toxicity of simvastatin and desloratadine, the major metabolite of loratadine, to muscle cells, and from the inhibitory effect of simvastatin acid, the active metabolite of simvastatin, on the hepatic uptake of desloratadine via OATP1B1/1B3. Our data not only identified unknown myopathic DDIs of clinical consequence, but also shed light on their underlying pharmacokinetic and pharmacodynamic mechanisms. More importantly, our approach exemplified a new strategy for identification and investigation of DDIs, one that combined literature mining using bioinformatic algorithms, ADR detection using a pharmacoepidemiologic design, and mechanistic studies employing in vitro experimental models.

Indiana University-Purdue University Indianapolis (IUPUI) / Model-based drug development has become an essential element to potentially make drug development more productive by assessing the data using mathematical and statistical approaches to construct and utilize models to increase the understanding of the drug and disease. The modeling and simulation approach not only quantifies the exposure-response relationship, and the level of variability, but also identifies the potential contributors to the variability. I hypothesized that the modeling and simulation approach can: 1) leverage our understanding of pharmacokinetic-pharmacodynamic (PK-PD) relationship from pre-clinical system to human; 2) quantitatively capture the drug impact on patients; 3) evaluate clinical trial designs; and 4) identify potential contributors to drug toxicity and efficacy. The major findings for these studies included: 1) a translational PK modeling approach that predicted clozapine and norclozapine central nervous system exposures in humans relating these exposures to receptor binding kinetics at multiple receptors; 2) a population pharmacokinetic analysis of a study of sertraline in depressed elderly patients with Alzheimer’s disease that identified site specific differences in drug exposure contributing to the overall variability in sertraline exposure; 3) the utility of a longitudinal tumor dynamic model developed by the Food and Drug Administration for predicting survival in non-small cell lung cancer patients, including an exploration of the limitations of this approach; 4) a Monte Carlo clinical trial simulation approach that was used to evaluate a pre-defined oncology trial with a sparse drug concentration sampling schedule with the aim to quantify how well individual drug exposures, random variability, and the food effects of abiraterone and nilotinib were determined under these conditions; 5) a time to event analysis that facilitated the identification of candidate genes including polymorphisms associated with vincristine-induced neuropathy from several association analyses in childhood acute lymphoblastic leukemia (ALL) patients; and 6) a LASSO penalized regression model that predicted vincristine-induced neuropathy and relapse in ALL patients and provided the basis for a risk assessment of the population. Overall, results from this dissertation provide an improved understanding of treatment effect in patients with an assessment of PK/PD combined and with a risk evaluation of drug toxicity and efficacy.