Modeling Exposure to Understand and Predict Kidney Injury

Quantitative systems toxicology modeling in pharmaceutical research and development: An industry-wide survey and selected case study examples

Quantitative systems toxicology (QST) models are increasingly being applied for predicting and understanding toxicity liabilities in pharmaceutical research and development. A European Federation of Pharmaceutical Industries and Associations (EFPIA)-wide survey was completed by 15 companies. The results provide insights into the current use of QST models across the industry. 73% of responding companies with more than 10,000 employees utilize QST models. The most applied QST models are for liver, cardiac electrophysiology, and bone marrow/hematology. Responders indicated particular interest in QST models for the central nervous system (CNS), kidney, lung, and skin. QST models are used to support decisions in both preclinical and clinical stages of pharmaceutical development. The survey suggests high demand for QST models and resource limitations were indicated as a common obstacle to broader use and impact. Increased investment in QST resources and training may accelerate application and impact. Case studies of QST model use in decision-making within EFPIA companies are also discussed. This article aims to (i) share industry experience and learnings from applying QST models to inform decision-making in drug discovery and development programs, and (ii) share approaches taken during QST model development and validation and compare these with recommendations for modeling best practices and frameworks proposed in the literature. Discussion of QST-specific applications in relation to these modeling frameworks is relevant in the context of the recently proposed International Council for Harmonization (ICH) M15 guideline on general principles for Model-Informed Drug Development (MIDD).

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Prediction of Janagliflozin Pharmacokinetics in Type 2 Diabetes Mellitus Patients with Liver Cirrhosis or Renal Impairment Using a Physiologically Based Pharmacokinetic Model

Janagliflozin is a sodium-glucose cotransporter 2 (SGLT2) inhibitor for type 2 diabetes mellitus (T2DM). The janagliflozin pharmacokinetics (PK) in T2DM patients with cirrhosis or renal impairment (RI) are unknown. To predict the janagliflozin PK in these patients, we constructed a physiologically based PK (PBPK) model that predicted the janagliflozin PK in normal animals. The model was extrapolated to healthy humans and optimized with the measured data. A PBPK model for T2DM patients was developed and optimized with the measured data. Based on the physiological alterations in cirrhosis or RI patients, the T2DM model was applied to predict the janagliflozin PK in these patients. Results were validated using fold error values. The predicted AUC values were 21880, 24881, 26996, and 28419 ng/ml·h in T2DM patients with no cirrhosis, Child-Pugh-A, B, and C, respectively, and those in T2DM patients with RI-mild, RI-moderate, and RI-severe were 21810, 21840, and 22845 ng/ml·h, respectively. Janagliflozin exposure increased with increasing cirrhosis severity, whereas it remained stable regardless of the RI severity. The PBPK model predicted the janagliflozin PK in patients with T2DM and liver cirrhosis or RI. Dose adjustment is less critical for these patients. Risk benefit assessment in janagliflozin dosing for T2DM patients with liver disease is recommended.

Development of Physiologically Based Pharmacokinetic Model for Pregabalin to Predict the Pharmacokinetics in Pediatric Patients with Renal Impairment and Adjust Dosage Regimens: PBPK Model of Pregabalin in Pediatric Patients with Renal Impairment

Pregabalin (PGB) is widely used clinically; however, its pharmacokinetics (PK) has not been studied in pediatric patients with renal impairment (RI). To design optimized PGB regimens for pediatric patients with varying degrees of RI and predict exposure to PGB, physiologically based pharmacokinetic (PBPK) models of PGB were developed and verified, and its disposition was simulated in the healthy population and adults with RI. The simulated results from the PBPK models after single-dose and multi-dose administrations of PGB were consistent with the corresponding observed data based on the fold error values of less than 2. The area under curve ratios were 1.23 ± 0.06, 2.02 ± 0.10, 3.86 ± 0.21, and 9.92 ± 0.79 in pediatric patients with mild, moderate, severe, and end-stage RI, respectively. Based on the predictions for pediatric patients with moderate, severe, and end-stage RI, the maximum dose should not exceed 7, 3.5, and 1.4 mg/kg/day, respectively, among those weighing < 30 kg, and it should not exceed 5, 2.5, and 1 mg/kg/day, respectively, among those weighing > 30 kg. In conclusion, the developed PBPK model is a valuable tool for predicting PGB dosage for pediatric patients with RI.

A Physiologically Based in Silico Tool to Assess the Risk of Drug-Related Crystalluria

Drug precipitation in the nephrons of the kidney can cause drug-induced crystal nephropathy (DICN). To aid mitigation of this risk in early drug discovery, we developed a physiologically based in silico model to predict DICN in rats, dogs, and humans. At a minimum, the likelihood of DICN is determined by the level of systemic exposure to the molecule, the molecule's physicochemical properties and the unique physiology of the kidney. Accordingly, the proposed model accounts for these properties in order to predict drug exposure relative to solubility along the nephron. Key physiological parameters of the kidney were codified in a manner consistent with previous reports. Quantitative structure-activity relationship models and in vitro assays were used to estimate drug-specific physicochemical inputs to the model. The proposed model was calibrated against urinary excretion data for 42 drugs, and the utility for DICN prediction is demonstrated through application to 20 additional drugs.