An IQ Industry Perspective on Informing Dosing Recommendations in Patients With Renal Impairment

The IQ CPLG Organ Impairment WG conducted a survey to understand how various IQ member companies develop dosing recommendations for patients with renal impairment. Eighteen IQ member companies participated in the survey. The survey results were summarized by the working group in light of the regulatory renal impairment guidance documents as well as recent publications from the pharmaceutical industry and nephrology community. There were two important learnings from the survey: (i) pharmaceutical companies do not use a consistent methodology to assess renal function in their drug development programs. (ii) there has been significant improvement in predicting how kidney function affects drug pharmacokinetics (PK) and thus dose recommendations. Applying model-based methods such as population PK, physiologically-based PK (PBPK), and virtual controls has enabled earlier prediction of how kidney function influences PK, leading to the participation of more patients with impaired kidney function in Phase 2 and 3 trials.

Operating Characteristics of the Simulated Healthy Participant Approach in Impaired Clearance Studies

Minimizing harm is a cornerstone of ethical research practices. A drug that has undergone extensive clinical pharmacological testing in healthy participants (HPs) and a diverse selection of patients can be described with a sufficiently predictive population pharmacokinetic (PopPK) model. In impaired clearance trials, recruitment is minimized and underpowered for all but major exposure differences. Virtual HP arms have been reported to support similar conclusions to conventional impaired clearance studies, and further minimize potential harm of drug exposure without medical benefit by eliminating an arm of the study. However, the extent to which the conventional analysis of impairment studies compare to the simulation approach is unknown. Here we assess the operating characteristics of the virtual cohort approach along with the conventional approach through controlled simulations. These simulations included a simple, widely accessible PopPK model and several internal models that have been used in a previous meta-analysis of the virtual cohort approach. In the pairwise comparisons assessed, the virtual cohort simulation approach had greater power per sample size than the conventional approach and the same power under the null hypothesis. Given key methodological differences, it is recommended that the simulation and conventional approaches be treated as having approximately the same power under equivalent conditions. These results provide a strong justification for the use of the virtual cohort approach when an adequate PopPK model is available, minimizing unnecessary exposure to study drugs that will not benefit healthy study participants.

Simulating Healthy Participant Pharmacokinetics for Renal and Hepatic Impairment Studies: Retrospective Assessment of the Approach

The recruitment of a parallel, healthy participants (HPs) arm in renal and hepatic impairment (RI and HI) studies is a common strategy to assess differences in pharmacokinetics. Limitations in this approach include the underpowered estimate of exposure differences and the use of the drug in a population for which there is no benefit. Recently, a method was published by Purohit et. al. (2023) that leveraged prior population pharmacokinetic (PopPK) modeling-based simulation to infer the distribution of exposure ratios between the RI/HI arms and HPs. The approach was successful, but it was a single example with a robust model having several iterations of development and fitting to extensive HP data. To test in more studies and models at different stages of development, our catalogue of RI/HI studies was searched, and those with suitable properties and from programs with available models were analyzed with the simulation approach. There were 9 studies included in the analysis. Most studies were associated with models that would have been available at the time (ATT) of the study, and all had a current, final model. For 3 studies, the HP PK was not predicted well by the ATT (2) or final (1) models. In comparison to conventional analysis of variance (ANOVA), the simulation approach provided similar point estimates and confidence intervals of exposure ratios. This PopPK based approach can be considered as a method of choice in situations where the simulation of HP data would not be an extrapolation, and when no other complicating factors are present.

Simulation of Febuxostat Pharmacokinetics in Healthy Subjects and Patients with Impaired Kidney Function Using Physiologically Based Pharmacokinetic Modeling

Febuxostat is recommend by the American College of Rheumatology Gout Management Guidelines as a first-line therapy for lowering the level of urate in patients with gout. At present, this drug is being prescribed mainly based on the clinical experience of doctors. The potential effects of clinical and demographic variables on the bioavailability and therapeutic effectiveness of febuxostat are not being considered. In this study, a physiologically based pharmacokinetic (PBPK) model of febuxostat was developed, thereby providing a theoretical basis for the individualized dosing of this drug in gout patients. The plasma concentration-time profiles corresponding to healthy subjects and gout patients with normal kidney function were simulated and validated; then, the model was used to predict the pharmacokinetic (PK) data of the drug in gout patients suffering from varying degrees of impaired kidney function. The error values (the predicted value/observed value) were used to validate the simulated PK parameters predicted by the PBPK model, including the area under the plasma concentration-time curve, the maximum plasma concentration, and time to maximum plasma concentration. Considering that to all error fold changes were smaller than 2 the PBPK model was. In subjects suffering from mild kidney impairment, moderate kidney impairment, severe kidney impairment, and end-stage kidney disease (ESRD), the predicted AUC_0-24h values increased by 1.62, 1.74, 2.27, and 2.65-fold, respectively, compared to gout patients with normal kidney function. Overall, the results showed that the PBPK model constructed in this study predict the pharmacokinetic changes in gout patients suffering from varying degrees of impaired kidney function. This article is protected by copyright. All rights reserved.

Effect of Chronic Kidney Disease on the Renal Secretion via Organic Anion Transporters 1/3: Implications for Physiologically-Based Pharmacokinetic Modeling and Dose Adjustment

There is growing evidence that active tubular secretory clearance (CL_s) may not decline proportionally with the glomerular filtration rate (GFR) in chronic kidney disease (CKD), leading to the overestimation of renal clearance (CL_r) when using solely GFR to approximate disease effect on renal elimination. The clinical pharmacokinetic data of 33 renally secreted OAT1/3 substrates were collated to investigate the impact of mild, moderate, and severe CKD on CL_r, tubular secretion and protein binding (f_u,p). The f_u,p of the collated substrates ranged from 0.0026 to 1.0 in healthy populations; observed CKD‐related increase in the f_u,p (up to 2.7‐fold) of 8 highly bound substrates (f_u,p ≤ 0.2) was accounted for in the analysis. Use of prediction equation based on disease‐related changes in albumin resulted in underprediction of the CKD‐related increase in f_u,p of highly bound substrates, highlighting the necessity to measure protein binding in severe CKD. The critical analysis of clinical data for 33 OAT1/3 probes established that decrease in OAT1/3 activity proportional to the changes in GFR was insufficient to recapitulate effects of severe CKD on unbound tubular secretion clearance. OAT1/3‐mediated CL_s was estimated to decline by an additional 50% relative to the GFR decline in severe CKD, whereas change in active secretion in mild and moderate CKD was proportional to GFR. Consideration of this additional 50% decline in OAT1/3‐mediated CL_s is recommended for physiologically‐based pharmacokinetic models and dose adjustment of OAT1/3 substrates in severe CKD, especially for substrates with high contribution of the active secretion to CL_r.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

PBPK Simulation-Based Evaluation of Ganciclovir Crystalluria Risk Factors: Effect of Renal Impairment, Old Age, and Low Fluid Intake

Dosing guidance is often lacking for chronic kidney disease (CKD) due to exclusion of such patients from pivotal clinical trials. Physiologically based pharmacokinetic (PBPK) modelling supports model-informed dosing when clinical data are lacking, but application of these approaches to patients with impaired renal function is not yet at full maturity. In the current study, a ganciclovir PBPK model was developed for patients with normal renal function and extended to CKD population. CKD-related changes in tubular secretion were explored in the mechanistic kidney model and implemented either as proportional or non-proportional decline relative to GFR. Crystalluria risk was evaluated in different clinical settings (old age, severe CKD and low fluid intake) by simulating ganciclovir medullary collecting duct (MCD) concentrations. The ganciclovir PBPK model captured observed changes in systemic pharmacokinetic endpoints in mild-to-severe CKD; these trends were evident irrespective of assumed pathophysiological mechanism of altered active tubular secretion in the model. Minimal difference in simulated ganciclovir MCD concentrations was noted between young adult and geriatric populations with normal renal function and urine flow (1 mL/min), with lower concentrations predicted for severe CKD patients. High crystalluria risk was identified at reduced urine flow (0.1 mL/min) as simulated ganciclovir MCD concentrations exceeded its solubility (2.6–6 mg/mL), irrespective of underlying renal function. The analysis highlighted the importance of appropriate distribution of virtual subjects’ systems data in CKD populations. The ganciclovir PBPK model illustrates the ability of this translational tool to explore individual and combined effects of age, urine flow, and renal impairment on local drug renal exposure.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

Development and evaluation of physiologically based pharmacokinetic drug-disease models for predicting captopril pharmacokinetics in chronic diseases

The advancement in the processing speeds of computing machines has facilitated the development of complex physiologically based pharmacokinetic (PBPK) models. These PBPK models can incorporate disease-specific data and could be used to predict pharmacokinetics (PK) of administered drugs in different chronic conditions. The present study aimed to develop and evaluate PBPK drug-disease models for captopril after incorporating relevant pathophysiological changes occurring in adult chronic kidney disease (CKD) and chronic heart failure (CHF) populations. The population-based PBPK simulator Simcyp was used as a modeling and simulation platform. The visual predictive checks and mean observed/predicted ratios (ratio(Obs/pred)) of the PK parameters were used for model evaluation. The developed disease models were successful in predicting captopril PK in all three stages of CKD (mild, moderate, and severe) and CHF, as the observed and predicted PK profiles and the ratio(obs/pred) for the PK parameters were in close agreement. The developed captopril PBPK models can assist in tailoring captopril dosages in patients with different disease severity (CKD and CHF).

Physiologically based pharmacokinetic model of renally cleared antibacterial drugs in Chinese renal impairment patients

To preliminarily develop physiologically based population models for Chinese renal impairment patients and to evaluate the prediction performance of new population models by renally cleared antibacterial drugs. First, demographic data and physiological parameters of Chinese renal impairment patients were collected, and then the coefficients of the relative demographic and physiological equation were recalibrated to construct the new population models. Second, drug‐independent parameters of ceftazidime, cefodizime, vancomycin, and cefuroxime were collected and verified by Chinese healthy volunteers, Caucasian healthy volunteers, and Caucasian renal impairment population models built in Simcyp. Finally, the newly developed population models were applied to predict the plasma concentration of four antibacterial drugs in Chinese renal impairment patients. The new physiologically based pharmacokinetic (PBPK) population models can predict the main pharmacokinetic parameters, including area under the plasma concentration–time curve extrapolated to infinity (AUC_inf), renal clearance (CL_r), and peak concentration (C_max), of ceftazidime, cefodizime, vancomycin, and cefuroxime following intravenous administrations with less than twofold error in mild, moderate, and severe Chinese renal impairment patients. The accuracy and precision of the predictions were improved compared with the Chinese healthy volunteers and Caucasian renal impairment population models. The PBPK population models were preliminarily developed and the first‐step validation results of four antibacterial drugs following intravenous administration showed acceptable accuracy and precision. The population models still need more systematic validation by using more drugs and scenarios in future studies to support their applications on dosage recommendation for Chinese renal impairment patients.

Physiologically-Based Pharmacokinetic Modelling of Creatinine-Drug Interactions in the Chronic Kidney Disease Population

Elevated serum creatinine (S_Cr ) caused by the inhibition of renal transporter(s) may be misinterpreted as kidney injury. The interpretation is more complicated in patients with chronic kidney disease (CKD) due to altered disposition of creatinine and renal transporter inhibitors. A clinical study was conducted in 17 patients with CKD (estimated glomerular filtration rate 15-59 mL/min/1.73 m² ); changes in S_Cr were monitored during trimethoprim treatment (100-200 mg/day), administered to prevent recurrent urinary infection, relative to the baseline level. Additional S_Cr -interaction data with trimethoprim, cimetidine, and famotidine in patients with CKD were collated from the literature. Our published physiologically-based creatinine model was extended to predict the effect of the CKD on S_Cr and creatinine-drug interaction. The creatinine-CKD model incorporated age/sex-related differences in creatinine synthesis, CKD-related glomerular filtration deterioration; change in transporter activity either proportional or disproportional to glomerular filtration rate (GFR) decline were explored. Optimized models successfully recovered baseline S_Cr from 64 patients with CKD (geometric mean fold-error of 1.1). Combined with pharmacokinetic models of inhibitors, the creatinine model was used to simulate transporter-mediated creatinine-drug interactions. Use of inhibitor unbound plasma concentrations resulted in 66% of simulated S_Cr interaction data within the prediction limits, with cimetidine interaction significantly underestimated. Assuming that transporter activity deteriorates disproportional to GFR decline resulted in higher predicted sensitivity to transporter inhibition in patients with CKD relative to healthy patients, consistent with sparse clinical data. For the first time, this novel modelling approach enables quantitative prediction of S_Cr in CKD and delineation of the effect of disease and renal transporter inhibition in this patient population.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL_T) = CL_T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

Novel Mechanistic PBPK Model to Predict Renal Clearance in Varying Stages of CKD by Incorporating Tubular Adaptation and Dynamic Passive Reabsorption

Chronic kidney disease (CKD) has significant effects on renal clearance (CLr ) of drugs. Physiologically-based pharmacokinetic (PBPK) models have been used to predict CKD effects on transporter-mediated renal active secretion and CLr for hydrophilic nonpermeable compounds. However, no studies have shown systematic PBPK modeling of renal passive reabsorption or CLr for hydrophobic permeable drugs in CKD. The goal of this study was to expand our previously developed and verified mechanistic kidney model to develop a universal model to predict changes in CLr in CKD for permeable and nonpermeable drugs that accounts for the dramatic nonlinear effect of CKD on renal passive reabsorption of permeable drugs. The developed model incorporates physiologically-based tubular changes of reduced water reabsorption/increased tubular flow rate per remaining functional nephron in CKD. The final adaptive kidney model successfully (absolute fold error (AFE) all < 2) predicted renal passive reabsorption and CLr for 20 permeable and nonpermeable test compounds across the stages of CKD. In contrast, use of proportional glomerular filtration rate reduction approach without addressing tubular adaptation processes in CKD to predict CLr generated unacceptable CLr predictions (AFE = 2.61-7.35) for permeable compounds in severe CKD. Finally, the adaptive kidney model accurately predicted CLr of para-amino-hippuric acid and memantine, two secreted compounds, in CKD, suggesting successful integration of active secretion into the model, along with passive reabsorption. In conclusion, the developed adaptive kidney model enables mechanistic predictions of in vivo CLr through CKD progression without any empirical scaling factors and can be used for CLr predictions prior to assessment of drug disposition in renal impairment.

Drug Dosing Recommendations for All Patients: A Roadmap for Change

Most drug labels do not contain dosing recommendations for a significant portion of real‐world patients for whom the drug is prescribed. Current label recommendations predominately reflect the population studied in pivotal trials that typically exclude patients who are very young or old, emaciated or morbidly obese, pregnant, or have multiple characteristics likely to influence dosing. As a result, physicians may need to guess the correct dose and regimen for these patients. It is now feasible to provide dose and regimen recommendations for these patients by integrating available scientific knowledge and by utilizing or modifying current regulatory agency‐industry practices. The purpose of this commentary is to explore several factors that should be considered in creating a process that will provide more effective, safe, and timely drug dosing recommendations for most, if not all, patients. These factors include the availability of real‐world data, development of predictive models, experience with the US Food and Drug Administration (FDA)’s pediatric exclusivity program, development of clinical decision software, funding mechanisms like the Prescription Drug Users Fee Act (PDUFA), and harmonization of global regulatory policies. From an examination of these factors, we recommend a relatively simple, efficient expansion of current practices designed to predict, confirm, and continuously improve drug dosing for more patients. We believe implementing these recommendations will benefit patients, payers, industry, and regulatory agencies.

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, "What does it take to make MIPD common practice?" Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.