Mycophenolic acid (MPA) is an approved immunosuppressive agent widely prescribed to prevent rejection after kidney transplantation. Wide between-subject variability (BSV) in MPA exposure exists which in part may be due to variability in enterohepatic recirculation (EHC). Several modeling strategies were developed to evaluate EHC as part of MPA pharmacokinetics, however mechanistic representation of EHC is limited. These models have not provided a satisfactory representation of the physiology of EHC in their modeling assumptions. The aim of this study was i) to develop an integrated model of MPA (total and unbound) and its metabolites (MPAG and acyl-MPAG) in kidney recipients, where this model provides a more physiological representation of EHC process, and ii) to evaluate the effect of donor and recipient clinical covariates and genotypes on MPA disposition. A five-compartment model with first-order input into an unbound MPA compartment connected to the MPAG, acyl-MPAG, and gallbladder compartment best fit the data. To represent the EHC process, the model was built based on the physiological concepts related to the hepatobiliary system and the gallbladder filling and emptying processes. The effect of cyclosporine versus tacrolimus on clearance of unbound MPA was included in the base model. Covariate analysis showed creatinine clearance to be significant on oral clearance of unbound MPA. The hepatic nuclear factor 1 alpha (HNF1A) genetic single nucleotide polymorphism (SNP) (rs2393791) in the recipient significantly affected the fraction of enterohepatically-circulated drug. Oral clearance of MPAG was affected by recipient IMPDH1 SNP (rs2288553), diabetes at the time of transplant, and donor sex.

The information generated by this study provides a basis for predicting the expected oral PK profiles of GSK3191607 in man and supports decisions on the future clinical development of the compound.

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Combination therapies have become increasingly researched and used in the treatment and management of complex diseases due to their ability to increase the chances for better efficacy and decreased toxicity. To evaluate drug combinations in drug development, pharmacokinetic and pharmacodynamic interactions between drugs in combination can be quantified using mathematical models; however, it can be difficult to deduce which models to use and how to use them to aid in clinical trial simulations to simulate the effect of a drug combination. This review paper aims to provide an overview of the various methods used to evaluate combination drug interaction for use in clinical trial development and a practical guideline on how combination modeling can be used in the settings of clinical trials.

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In March 2020, hydroxychloroquine (HCQ) and azithromycin were tested as potential treatments for in an open label non-randomized clinical trial [1]. Data were collected from 36 confirmed COVID-19 patients after receiving no treatment (n = 16) or 200 mg q8h of HCQ with (n = 6) or without (n = 14) azithromycin depending on clinical status. Collected data over six days included one HCQ concentration per patient; respiratory viral loads (PCR assay); azithromycin co-treatment; and clinical status among other variables.Using observed HCQ concentrations and basic pharmacokinetic equations, it was possible to calculate missing concentrations at all days for all patients ( Supplementary Fig. S1). Daily viral load, presented as PCR threshold cycle (CT), was converted into a dichotomous variable (positive-PCR or negative-PCR). A logistic regression evaluated whether HCQ concentrations, azithromycin co-treatment (yes/no), clinical status (asymptomatic, upper or lower respiratory tract infection [URTI or LRTI]), time (day 0-6), and other covariates (age, sex) were associated with response (positive-PCR outcome).Model evaluation and selection was performed based on statistical significance (p-value B 0.05) and diagnostic plots ( Supplementary Figs. S2, S3). The final model contained HCQ log-concentrations (ng/ml), azithromycin cotreatment, clinical status, and day as statistically significant covariates ( Figs. 1 and S4).Results showed that the odds of positive-PCR decrease by 53% for each unit increase in HCQ log-concentration. Similarly, the odds decrease by 61%, and by 12% for each day increase, and for azithromycin co-treatment, respectively. Whereas the odds of positive-PCR increase by 99.4 folds and 212 folds for having URTI and LRTI versus being asymptomatic subject.Using the model, minimum HCQ concentrations to achieve [ 50% probability of negative-PCR on day 3 were calculated, with or without azithromycin, for the clinical statuses. A minimum HCQ concentration (ng/ml) of 8103 (without azithromycin) and 282 (with azithromycin) are needed in subjects with LRTI. A minimum HCQ concentration (ng/ml) of 2441 (without azithromycin) and 83.9 (with azithromycin) are needed in subjects with URTI. Asymptomatic patients require minimal HCQ concentrations (\ 2 ng/ml) regardless of azithromycin co-treatment.Next, HCQ population PK model [2] was used to simulate dosing regimens needed to achieve targeted HCQ concentrations. Simulations showed that when HCQ is coadministered with azithromycin, a loading dose is critical in rapid achievement of targeted concentrations. The suggested dosing regimen for HCQ (with azithromycin), is 600 mg at 0 and 400 at 8 h followed by 200 mg q8h ( Supplementary Fig. S5). When HCQ is administered without azithromycin, no safe and suitable HCQ dose can achieve targeted concentrations in LRTI and URTI patients ( Supplementary Fig. S6).The analysis confirms that co-treatment of COVID-19 with HCQ and azithromycin increases the probability of negative-PCR in patients. It also shows that clinical status af...