Simcyp (United Kingdom)

Antisense oligonucleotide (ASO) therapeutics present new opportunities for treating challenging-to-treat diseases. Prediction of concentration-time course in systemic circulation and tissues within the context of large molecule physiologically based pharmacokinetic (PBPK) modelling has proven to be useful in animal-to-human extrapolation and first-in-human dose selection. A human PBPK model was developed and verified using bepirovirsen clinical data to predict plasma pharmacokinetics (PK) and tissue concentrations. Liver and kidney partition coefficient ratios from monkey studies, corrected for plasma unbound fraction in monkeys and humans, informed bepirovirsen concentration predictions in the human liver and kidney. Liver and kidney partition coefficients were calculated to be 2147 and 2822, respectively. All predicted PK parameters in healthy volunteers (except tmax) were within two-folds of observed data. Predicted vs. observed clearance (L/h), AUC0-inf (µg.h/mL) and Cmax (µg/mL) for the 300 mg single dose in healthy volunteers were 2.94, 114.43, and 10.56 vs. 2.25, 136.6, and 6.5, respectively. The majority of observed plasma concentrations for all doses were within the 5th and 95th percentiles of the predictions. The evaluated model was used to predict the impact of moderate hepatic impairment on bepirovirsen PK in virtual patients. The predicted vs. observed bepirovirsen exposure in moderate hepatic impairment was 0.9 and 0.7-fold lower, respectively, compared with healthy volunteers. The PBPK model predicted the liver and kidney tissue Cmax values to be 172 and 132 µg/mL, respectively. In conclusion, a PBPK modelling approach for bepirovirsen, an ASO, is presented in this article and offers opportunities for future applications to other oligonucleotide therapeutics.

Treatment of breastfeeding mothers with malaria is challenging due to the lack of information describing drug exposure in milk and the daily dose to the breastfed infant. Physiologically based pharmacokinetic (PBPK) modeling was used to predict milk-to-plasma (M/P) ratios, infant daily doses (IDD) and relative infant doses (RID) for five antimalarials with clinical lactation data (chloroquine, pyrimethamine, piperaquine, mefloquine and primaquine). In all cases, RID was correctly categorized as above or below the WHO proposed cut-off of 10% using two prediction models. Predicted M/P ratios were within 2-fold of observations for 63% of studies using both models (75% and 100% were within 3-fold for Models 1 and 2, respectively). M/P ratios, IDD and RID were predicted prospectively for seven antimalarials. RID was < 10% for amodiaquine, dihydroartemisinin, proguanil, and pyronaridine, and > 10% for lumefantrine and tafenoquine. For atovaquone, RID was > 10% with Model 1 but not Model 2. Predicted IDD were considerably lower than licensed doses for infants except for lumefantrine (Model 2) and tafenoquine (not licensed in < 2 years). Predictions were sensitive to drug properties (plasma protein binding and lipophilicity) and milk properties (creamatocrit and pH). This analysis demonstrates the utility of PBPK to predict milk exposure in the absence of clinical lactation information. These prediction methodologies can be used, alongside any licensed dosing information for < 1 year-olds, to evaluate whether a clinical lactation study is necessary and to inform drug label or policy recommendations. The ultimate goal is to better inform optimal treatment for lactating women supporting malaria eradication.

Prediction of hepatotoxicity in humans remains an unresolved challenge. Recently, an in vitro/in silico method was established to predict blood concentrations of test compounds with an increased risk of causing human hepatotoxicity. In the present study, we addressed the question whether gene expression data can improve the quality of hepatotoxicity prediction compared to cytotoxicity analysis alone. A particular challenge is that high-dimensional gene expression data must be sum­marized into variables that allow the determination of the lowest test compound concentration that causes altered gene expression. To address this challenge, we analyzed 60 hepatotoxic and non-hepatotoxic substances in a concentration-dependent manner for cytotoxicity and expression of 3,524 probes previously reported to be influenced by hepatotoxicants. The toxicity separation index (TSI) was applied to quantify how well specific summary variables of gene expression can differen­tiate between the set of hepatotoxic and non-hepatotoxic substances. The best TSI was obtained when the lowest concentration of a test compound that led to differential expression of two genes when compared to vehicle controls was considered positive. Furthermore, the best gene expression-based summary variable was superior to cytotoxicity-based variables alone, and the combination of the best summary variables of gene expression and cytotoxicity data further improved the TSI compared to each category alone. In conclusion, the method used to derive summary variables of gene expression is critical, and the best summary variables improve the prediction of hepatotoxic substances in relation to oral doses and blood concentrations in humans.

The utility of physiologically based pharmacokinetic (PBPK) models in support of drug development has been well documented. During the discovery stage, PBPK modeling has increasingly been applied for early risk assessment, prediction of human dose, toxicokinetic dose projection, and early formulation assessment. Previous review articles have proposed model-building and application strategies for PBPK-based first-in-human predictions with comprehensive descriptions of the individual components of PBPK models. This includes the generation of decision trees based on literature reviews to guide the application of PBPK models in the discovery setting. The goal of this minireview is to provide additional guidance on the real-world application of PBPK models in support of the discovery stage of drug development, to assist in decision making. We have illustrated our recommended approach through description of case examples where PBPK models have been successfully applied to aid in human pharmacokinetic projection, candidate selection, and prediction of drug interaction liability for parent and metabolite. Through these case studies, we have highlighted fundamental issues, including preverification in preclinical species, the application of empirical scalars in the prediction of invivo clearance from invitro systems, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed. SIGNIFICANCE STATEMENT: Through description of 3 case studies, this minireview highlights the fundamental principles of physiologically based pharmacokinetic application during drug discovery. These include preverification of the model in preclinical species, application of empirical scalars where necessary in the prediction of clearance, in silico prediction of permeability, and the exploration of aqueous and biorelevant solubility data to predict dissolution. In addition, current knowledge gaps have been highlighted and future directions proposed.

Prediction of hepatotoxicity in humans remains an unresolved challenge. Recently, an in vitro/in silico method was established to predict blood concentrations of test compounds with an increased risk of causing human hepatotoxicity. In the present study, we addressed the question whether gene expression data can improve the quality of hepatotoxicity prediction compared to cytotoxicity analysis alone. A particular challenge is that high-dimensional gene expression data must be sum­marized into variables that allow the determination of the lowest test compound concentration that causes altered gene expression. To address this challenge, we analyzed 60 hepatotoxic and non-hepatotoxic substances in a concentration-dependent manner for cytotoxicity and expression of 3,524 probes previously reported to be influenced by hepatotoxicants. The toxicity separation index (TSI) was applied to quantify how well specific summary variables of gene expression can differen­tiate between the set of hepatotoxic and non-hepatotoxic substances. The best TSI was obtained when the lowest concentration of a test compound that led to differential expression of two genes when compared to vehicle controls was considered positive. Furthermore, the best gene expression-based summary variable was superior to cytotoxicity-based variables alone, and the combination of the best summary variables of gene expression and cytotoxicity data further improved the TSI compared to each category alone. In conclusion, the method used to derive summary variables of gene expression is critical, and the best summary variables improve the prediction of hepatotoxic substances in relation to oral doses and blood concentrations in humans. Plain language summary Liver injury is a relevant side effect of many drugs. It represents the most common cause of acute liver failure in Western countries as well as the most common reason for late-stage drug development failure or withdrawal of drugs from the market. We show that a genome-wide gene expression analysis of cultured human hepatocytes exposed to drugs can identify hepatotoxic and non-hepato­toxic substances. To achieve this, we have developed a technique to summarize all gene expression variables to determine the lowest concentration at which a test substance begins to influence gene expression. We find that the cell test must include the highest concentration of the drug that is intended to be reached in the blood of patients at therapeutic doses. This approach can reduce the number of animals needed in pre-clinical safety studies as liver-damaging drug candidates would be detected and eliminated prior to studies in rodents.

OATP1B facilitates the uptake of xenobiotics into hepatocytes and is a prominent target for drug–drug interactions (DDIs). Reduced systemic exposure of OATP1B substrates has been reported following multiple‐dose rifampicin; one explanation for this observation is OATP1B induction. Non‐uniform hepatic distribution of OATP1B may impact local rifampicin tissue concentrations and rifampicin‐mediated protein induction, which may affect the accuracy of transporter‐ and/or metabolizing enzyme‐mediated DDI predictions. We incorporated quantitative zonal OATP1B distribution data from immunofluorescence imaging into a PBPK modeling framework to explore rifampicin interactions with OATP1B and CYP substrates. PBPK models were developed for rifampicin, two OATP1B substrates, pravastatin and repaglinide (also metabolized by CYP2C8/CYP3A4), and the CYP3A probe, midazolam. Simulated hepatic uptake of pravastatin and repaglinide increased from the periportal to the pericentral region (approximately 2.1‐fold), consistent with OATP1B distribution data. Simulated rifampicin unbound intracellular concentrations increased in the pericentral region (1.64‐fold) compared to simulations with uniformly distributed OATP1B. The absolute average fold error of the rifampicin PBPK model for predicting substrate maximal concentration (Cmax) and area under the plasma concentration–time curve (AUC) ratios was 1.41 and 1.54, respectively (nine studies). In conclusion, hepatic OATP1B distribution has a considerable impact on simulated zonal substrate uptake clearance values and simulated intracellular perpetrator concentrations, which regulate transporter and metabolic DDIs. Additionally, accounting for rifampicin‐mediated OATP1B induction in parallel with inhibition improved model predictions. This study provides novel insight into the effect of hepatic OATP1B distribution on site‐specific DDI predictions and the impact of accounting for zonal transporter distributions within PBPK models.

Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens. Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant. The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects). A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs.

Buprenorphine readily crosses the placenta, and with greater prenatal exposure, neonatal opioid withdrawal syndrome (NOWS) likely grows more severe. Current dosing strategies can be further improved by tailoring doses to expected NOWS severity. To allow the conceptualization of fetal buprenorphine exposure, a maternal-fetal physiologically based pharmacokinetic (PBPK) model for sublingual buprenorphine was developed using Simcyp (v21.0). Buprenorphine transplacental passage was predicted from its physicochemical properties. The maternal-fetal PBPK model integrated reduced transmucosal absorption driven by lower salivary pH and induced metabolism observed during pregnancy. Maternal pharmacokinetics was adequately predicted in the second trimester, third trimester, and postpartum period, with the simulated area under the curve from 0 to 12 h, apparent clearance, and peak concentration falling within the 1.25-fold prediction error range. Following post hoc adjustment of the likely degree of individual maternal sublingual absorption, umbilical cord blood concentrations at delivery (n = 21) were adequately predicted, with a geometric mean ratio between predicted and observed fetal concentrations of 1.15 and with 95.2% falling within the 2-fold prediction error range. The maternal-fetal PBPK model developed in this study can be used to forecast fetal buprenorphine exposure and would be valuable to investigate its correlation to NOWS severity.