Novel Computational Methods to Discover Genes Linked to Drug Response

Metformin is used first line for treatment of type 2 diabetes (T2D) and is one of the most frequently prescribed drugs worldwide. As the global incidence of T2D rapidly increases, the low cost of metformin makes this treatment option particularly attractive in developing nations. Understanding metformin’s efficacy in different patient populations with diverse genetic backgrounds will be critical in managing this deleterious metabolic disorder. The major goal of this dissertation research was to use novel, quantitative approaches to elucidate genetic and non-genetic components that predict metformin disposition and glycemic response. As a first goal, the role of transcription factor variants on metformin pharmacokinetics and pharmacodynamics was investigated. From this analysis, five variants in SP1 were significantly associated with changes in treatment HbA1c (p < 0.01) and metformin secretory clearance (p < 0.05). Genetic variants in transcription factors PPAR-alpha and HNF4-alpha were significantly associated with HbA1c change only, but were not significantly associated with pharmacokinetics. A plausible biological mechanism by which genetic variants affected the pharmacological variation of metformin was determined using gene expression levels linked to genetic variants (eQTLs). The focus was on transporter expression. From this study, we discovered that genomic regions proximal to metformin transporters were linked to expression levels of SLC47A1, SLC22A3, and SLC22A2, with a potential transcription factor-binding hypothesis for SP1. We also found variants in transcription factor HNF4-alpha were the most influential trans-eQTLs, accounting for expression level variation in both SLC47A1 and SLC22A1. Finally, we developed a mathematical model to quantify disease progression on metformin therapy using HbA1c data with the goal of explaining long-term HbA1c variability through the investigation of genetic, demographic, and clinical factors. From this analysis, we found two SNPs in CSMD1 (rs2617102, rs2954625) and one SNP in SLC22A2 (rs316009) as significantly influencing the long-term variance in HbA1c. Overall, this dissertation research enhances our current knowledge of the pharmacogenetic landscape by expanding the set of pharmacologically relevant genes and providing a pharmacokinetic and biological basis for some of these genes. Future research will continue to focus on replication and uncovering the mechanism driving the pharmacological genes highlighted here

The Pharmacokinetic and Pharmacodynamic Properties of Hydroxychloroquine and Dose Selection for COVID-19: Putting the Cart Before the Horse

Abstract Coronavirus disease 2019 (COVID-19), caused by the 2019 novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently responsible for a global pandemic. To date, only remdesivir and dexamethasone have demonstrated a positive response in a prospective, randomized trial for the treatment of patients with COVID-19. Hydroxychloroquine (HCQ) is an agent available in an oral formulation with in vitro activity against SARS-CoV-2 that has been suggested as a potential agent. Unfortunately, results of randomized trials evaluating HCQ as treatment against a control group are lacking, and little is known about its pharmacokinetic/pharmacodynamic (PK/PD) profile against SARS-CoV-2. The objective of this review was to describe the current understanding of the PK/PD and dose selection of HCQ against SARS-CoV-2, discuss knowledge gaps, and identify future studies that are needed to optimize the efficacy and safety of treatments against COVID-19.http://deepblue.lib.umich.edu/bitstream/2027.42/173968/1/40121_2020_Article_325.pd

Assessment of a Model-Informed Precision Dosing Platform Use in Routine Clinical Care for Personalized Busulfan Therapy in the Pediatric Hematopoietic Cell Transplantation (HCT) Population.

IntroductionPopulation pharmacokinetic (PK) studies demonstrate model-based dosing for busulfan that incorporates body size and age improve clinical target attainment as compared to weight-based regimens. Recently, for clinical dosing of busulfan and TDM, our institution transitioned to a cloud-based clinical decision support tool (www.insight-rx.com). The goal of this study was to assess the dose decision tool for the achievement of target exposure of busulfan in children undergoing hematopoietic cell transplantation (HCT).Patients and methodsPatients (N = 188) were grouped into cohorts A, B, or C based on the method for initial dose calculation and estimation of AUC: Cohort A: Initial doses were based on the conventional dosing algorithm (as outlined in the manufacturers' package insert) and non-compartmental analysis (NCA) estimation using the trapezoidal rule for estimation of AUC following TDM. Cohort B: Initial doses for busulfan were estimated by a first-generation PK model and NCA estimation of AUC following TDM. Cohort C: Initial doses were calculated by an updated, second-generation PK model available in the dose decision tool with an estimation of AUC following TDM.ResultsThe percent of individuals achieving the exposure target at the time of first PK collection was higher in subjects receiving initial doses provided by the model-informed precision dosing platform (cohort C, 75%) versus subjects receiving initial doses based on either of the two other approaches (conventional guidelines/cohort A, 25%; previous population PK model and NCA parameter estimation, cohort B, 50%). Similarly, the percent of subjects achieving the targeted cumulative busulfan exposure (cAUC) in cohort C was 100% vs. 66% and 88% for cohort A and B, respectively. For cAUC, the variability in the spread of target attainment (%CV) was low at 4.1% for cohort C as compared to cohort A (14.8%) and cohort B (17.1%).ConclusionAchievement of goal exposure early on in treatment was improved with the updated model for busulfan and the Bayesian platform. Model-informed dosing and TDM utilizing a Bayesian-based platform provides a significant advantage over conventional guidelines for the achievement of goal cAUC exposure

The Effect of Famotidine, a MATE1-Selective Inhibitor, on the Pharmacokinetics and Pharmacodynamics of Metformin.

IntroductionPharmacokinetic outcomes of transporter-mediated drug-drug interactions (TMDDIs) are increasingly being evaluated clinically. The goal of our study was to determine the effects of selective inhibition of multidrug and toxin extrusion protein 1 (MATE1), using famotidine, on the pharmacokinetics and pharmacodynamics of metformin in healthy volunteers.MethodsVolunteers received metformin alone or with famotidine in a crossover design. As a positive control, the longitudinal effects of famotidine on the plasma levels of creatinine (an endogenous substrate of MATE1) were quantified in parallel. Famotidine unbound concentrations in plasma reached 1 µM, thus exceeding the in vitro concentrations that inhibit MATE1 [concentration of drug producing 50 % inhibition (IC50) 0.25 µM]. Based on current regulatory guidance, these concentrations are expected to inhibit MATE1 clinically [i.e. maximum unbound plasma drug concentration (C max,u)/IC50 >0.1].ResultsConsistent with MATE1 inhibition, famotidine administration significantly altered creatinine plasma and urine levels in opposing directions (p < 0.005). Interestingly, famotidine increased the estimated bioavailability of metformin [cumulative amount of unchanged drug excreted in urine from time zero to infinity (A e∞)/dose; p < 0.005] without affecting its systemic exposure [area under the plasma concentration-time curve (AUC) or maximum concentration in plasma (C max)] as a result of a counteracting increase in metformin renal clearance. Moreover, metformin-famotidine co-therapy caused a transient effect on oral glucose tolerance tests [area under the glucose plasma concentration-time curve between time zero and 0.5 h (AUCglu,0.5); p < 0.005].ConclusionsThese results suggest that famotidine may improve the bioavailability and enhance the renal clearance of metformin