Scaling Drug Clearance from Adults to the Young Children for Drugs Undergoing Hepatic Metabolism: A Simulation Study to Search for the Simplest Scaling Method

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Challenges of pediatric pharmacotherapy: A narrative review of pharmacokinetics, pharmacodynamics, and pharmacogenetics

PURPOSE

Personalized pharmacotherapy, including for the pediatric population, provides optimal treatment and has emerged as a major trend owing to advanced drug therapeutics and diversified drug selection. However, it is essential to understand the growth and developmental characteristics of this population to provide appropriate drug therapy. In recent years, clinical pharmacogenetics has accumulated knowledge in pediatric pharmacotherapy, and guidelines from professional organizations, such as the Clinical Pharmacogenetics Implementation Consortium, can be consulted to determine the efficacy of specific drugs and the risk of adverse effects. However, the existence of a large knowledge gap hinders the use of these findings in clinical practice.

METHODS

We provide a narrative review of the knowledge gaps in pharmacokinetics (PK) and pharmacodynamics (PD) in the pediatric population, focusing on the differences from the perspective of growth and developmental characteristics. In addition, we explored PK/PD in relation to pediatric clinical pharmacogenetics.

RESULTS

The lack of direct and indirect biomarkers for more accurate assessment of the effects of drug administration limits the current knowledge of PD. In addition, incorporating pharmacogenetic insights as pivotal covariates is indispensable in this comprehensive synthesis for precision therapy; therefore, we have provided recommendations regarding the current status and challenges of personalized pediatric pharmacotherapy. The integration of clinical pharmacogenetics with the health care system and institution of educational programs for health care providers is necessary for its safe and effective implementation. A comprehensive understanding of the physiological and genetic complexities of the pediatric population will facilitate the development of effective and personalized pharmacotherapeutic strategies.

Integration of Biorelevant Pediatric Dissolution Methodology into PBPK Modeling to Predict In Vivo Performance and Bioequivalence of Generic Drugs in Pediatric Populations: a Carbamazepine Case Study

This study investigated the impact of gastro-intestinal fluid volume and bile salt (BS) concentration on the dissolution of carbamazepine (CBZ) immediate release (IR) 100 mg tablets and to integrate these in vitro biorelevant dissolution profiles into physiologically based pharmacokinetic modelling (PBPK) in pediatric and adult populations to determine the biopredictive dissolution profile. Dissolution profiles of CBZ IR tablets (100 mg) were generated in 50-900 mL biorelevant adult fasted state simulated gastric and intestinal fluid (Ad-FaSSGF and Ad-FaSSIF), also in three alternative compositions of biorelevant pediatric FaSSGF and FaSSIF medias at 200 mL. This study found that CBZ dissolution was poorly sensitive to changes in the composition of the biorelevant media, where dissimilar dissolution (F2 = 46.2) was only observed when the BS concentration was changed from 3000 to 89 μM (Ad-FaSSIF vs Ped-FaSSIF 50% 14 BS). PBPK modeling demonstrated the most predictive dissolution volume and media composition to forecast the PK was 500 mL of Ad-FaSSGF/Ad-FaSSIF media for adults and 200 mL Ped-FaSSGF/FaSSIF media for pediatrics. A virtual bioequivalence simulation was conducted by using Ad-FaSSGF and/or Ad-FaSSIF 500 mL or Ped-FaSSGF and/or Ped-FaSSIF 200 mL dissolution data for CBZ 100 mg (reference and generic test) IR product. The CBZ PBPK models showed bioequivalence of the product. This study demonstrates that the integration of biorelevant dissolution data can predict the PK profile of a poorly soluble drug in both populations. Further work using more pediatric drug products is needed to verify biorelevant dissolution data to predict the in vivo performance in pediatrics.

A General Biphasic Bodyweight Model for Scaling Basal Metabolic Rate, Glomerular Filtration Rate, and Drug Clearance from Birth to Adulthood

The objective of this study is to propose a unified, continuous, and bodyweight-only equation to quantify the changes of human basal metabolic rate (BMR), glomerular filtration rate (GFR), and drug clearance (CL) from infancy to adulthood. The BMR datasets were retrieved from a comprehensive historical database of male and female subjects (0.02 to 64 years). The CL datasets for 17 drugs and the GFR dataset were generated from published maturation and growth models with reported parameter values. A statistical approach was used to simulate the model-generated CL and GFR data for a hypothetical population with 26 age groups (from 0 to 20 years). A biphasic equation with two power-law functions of bodyweight was proposed and evaluated as a general model using nonlinear regression and dimensionless analysis. All datasets universally reveal biphasic curves with two distinct linear segments on log-log plots. The biphasic equation consists of two reciprocal allometric terms that asymptotically determine the overall curvature. The fitting results show a superlinear scaling phase (asymptotic exponent >1; ca. 1.5-3.5) and a sublinear scaling phase (asymptotic exponent <1; ca. 0.5-0.7), which are separated at the phase transition bodyweight ranging from 5 to 20 kg with a mean value of 10 kg (corresponding to 1 year of age). The dimensionless analysis generalizes and offers quantitative realization of the maturation and growth process. In conclusion, the proposed mixed-allometry equation is a generic model that quantitatively describes the phase transition in the human maturation process of diverse human functions.

Allometric Scaling in Pharmacokinetic Studies in Anesthesiology

A clinical review is presented of basic allometric scaling theory and its application to pharmacokinetic models in anesthesia and other fields in the biologic sciences.

An update on the use of allometric and other scaling methods to scale drug clearance in children: towards decision tables

INTRODUCTION

When pediatric data are not available for a drug, allometric and other methods are applied to scale drug clearance across the pediatric age-range from adult values. This is applied when designing first-in-child studies, but also for off-label drug prescription.

AREAS COVERED

This review provides an overview of the systematic accuracy of allometric and other pediatric clearance scaling methods compared to gold-standard PBPK predictions. The findings are summarized in decision tables to provide a priori guidance on the selection of appropriate pediatric clearance scaling methods for both novel drugs for which no pediatric data are available and existing drugs in clinical practice.

EXPERT OPINION

While allometric scaling principles are commonly used to scale pediatric clearance, there is no universal allometric exponent (i.e., 1, 0.75 or 0.67) that can accurately scale clearance for all drugs from adults to children of all ages. Therefore, pediatric scaling decision tables based on age, drug elimination route, binding plasma protein, fraction unbound, extraction ratio, and/or isoenzyme maturation are proposed to a priori select the appropriate (allometric) clearance scaling method, thereby reducing the need for full PBPK-based clearance predictions. Guidance on allometric scaling when estimating pediatric clearance values is provided as well.

Physiologically-based pharmacokinetic modeling of nafamostat to support dose selection for treatment of pediatric patients with COVID-19

Pediatric patients with coronavirus disease 2019 (COVID-19) are increasing, and severe cases such as multisystem inflammatory syndrome are being reported. Nafamostat, a repurposing drug, is currently being explored for the treatment of COVID-19 in adults. However, the data supporting its exposure in pediatrics remains scarce. Physiologically-based pharmacokinetic (PBPK) modeling enables the prediction of drug exposure in pediatrics based on ontogeny of metabolic enzymes and age dependent anatomical and physiological changes. The study aimed to establish a PBPK model of nafamostat in adults, then scale the adult PBPK model to children for predicting pediatric exposures of nafamostat and an optimal weight-based nafamostat dose in pediatric population. The developed model adequately described adult exposure data in healthy volunteers following i.v. administration with three doses (10, 20, and 40 mg). Scaling adult PBPK models to five pediatric groups predicted that as age advances from neonate to adult, the exposure of nafamostat slightly increased from neonate to infant, steadily decreased from infant to child, and then increased from child to adult after the administration of 0.2 mg/kg/h for 14 days, a dosing regimen being conducted in a clinical trial for COVID-19. Based on the fold change of predicted area under the curve for the respective pediatric group over those of adults, weight-based dosages for each pediatric group may be suggested. The novel PBPK model described in this study may be useful to investigate nafamostat pharmacokinetics in a pediatric subgroup further.

Ontogeny of Scaling Factors for Pediatric Physiology-Based Pharmacokinetic Modeling and Simulation: Microsomal Protein Per Gram of Liver

Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom-up physiology-based pharmacokinetic modeling and simulation, but data in pediatrics are limited. Therefore, MPPGL was determined in 160 liver samples from pediatric (n = 129) and adult (n = 31) donors obtained from four sources: the University of Maryland Brain and Tissue Bank (UMBTB), tissue retrieval services at the University of Minnesota and University of Pittsburgh, and Sekisui-Xenotech. Tissues were homogenized and subjected to differential centrifugation to prepare microsomes, and cytochrome c reductase activities in tissue homogenates and microsomes were used to estimate cytochrome P450 reductase (POR) activity as a marker of microsomal recovery; microsomal POR content was also assessed by quantitative proteomics. MPPGL values varied 5- to 10-fold within various age groups/developmental stages, and tissue source was identified as a contributing factor. Using a "trimmed" dataset comprised of samples ranging from 3 to 18 years of age common to the four sources, POR protein abundance and activity in microsomes and POR activity in homogenates was lower in UMBTB samples (autopsy) compared with other sources (perfused/flash-frozen). Regression analyses revealed that the UMBTB samples were driving an apparent age effect as no effect of age on log-transformed MPPGL values was observed when the UMBTB samples were excluded. We conclude that a mean±SD MPPGL value of 30.4±1.7 mg/g is representative between one month postnatal age and early adulthood. Potential source effects should be considered for studies involving tissue samples from multiple sources with different procurement and processing procedures. SIGNIFICANCE STATEMENT: Microsomal protein per gram of liver (MPPGL) is an important scaling factor for bottom up PBPK modeling and simulation, but data in pediatrics are limited. Although MPPGL varies 5- to 10-fold at a given developmental stage, a value of 30.4 ± 1.7 mg/g (mean ± SD) is representative between one month postnatal age and early adulthood. However, when tissue samples are obtained from multiple sources, different procurement and processing procedures may influence the results and should be taken into consideration.

Physiologically-Based Pharmacokinetic Modeling of Remdesivir and Its Metabolites to Support Dose Selection for the Treatment of Pediatric Patients With COVID-19

Severe coronavirus disease 2019 (COVID‐19) disease, including multisystem inflammatory syndrome, has been reported in children. This report summarizes development of a remdesivir physiologically‐based pharmacokinetic (PBPK) model that accurately describes observed adult remdesivir and metabolites exposure and predicts pediatric remdesivir and metabolites exposure. The adult PBPK model was applied to predict pediatric remdesivir and metabolites steady‐state exposures using the Pediatric Population Model in SimCYP and incorporated the relevant physiologic and mechanistic information. Model development was based on adult phase I exposure data in healthy volunteers who were administered a 200‐mg loading dose of remdesivir intravenous (IV) over 0.5 hours on Day 1, then 100‐mg daily maintenance doses of IV over 0.5 hours starting on Day 2 and continuing through Days 5 or 10. Simulations indicated that use of the adult therapeutic remdesivir dosage regimen (200‐mg loading dose on Day 1 then 100‐mg daily maintenance dose starting on Day 2) in pediatric patients ≥ 40 kg and a weight‐based remdesivir dosage regimen (5‐mg/kg loading dose on Day 1 then 2.5‐mg/kg daily maintenance dose starting on Day 2) in pediatric patients weighing 2.5 to < 40 kg is predicted to maintain therapeutic exposures of remdesivir and its metabolites. The comprehensive PBPK model described in this report supported remdesivir dosing in planned pediatric clinical studies and dosing in the emergency use authorization and pediatric compassionate use programs that were initiated to support remdesivir as a treatment option during the pandemic.

Pediatric Pharmacokinetics and Dose Predictions: A Report of a Satellite Meeting to the 10th Juvenile Toxicity Symposium

On April 24, 2019, a symposium on Pediatric Pharmacokinetics and Dose Predictions was held as a satellite meeting to the 10th Juvenile Toxicity Symposium. This symposium brought together scientists from academia, industry, and clinical research organizations with the aim to update each other on the current knowledge on pediatric drug development. Through more knowledge on specific ontogeny profiles of drug metabolism and transporter proteins, integrated into physiologically-based pharmacokinetic (PBPK) models, we have gained a more integrated understanding of age-related differences in pharmacokinetics (PKs), Relevant examples were presented during the meeting. PBPK may be considered the gold standard for pediatric PK prediction, but still it is important to know that simpler methods, such as allometry, allometry combined with maturation function, functions based on the elimination pathway, or linear models, also perform well, depending on the age range or the mechanisms involved. Knowledge from different methods and information sources should be combined (e.g., microdosing can reveal early read-out of age-related differences in exposure), and such results can be a value to verify models. To further establish best practices for dose setting in pediatrics, more in vitro and in vivo research is needed on aspects such as age-related changes in the exposure-response relationship and the impact of disease on PK. New information coupled with the refining of model-based drug development approaches will allow faster targeting of intended age groups and allow more efficient design of pediatric clinical trials.

Ontogeny of Phase I Metabolism of Drugs

Capturing ontogeny of enzymes involved in phase I metabolism is crucial to improve prediction of dose-concentration and concentration-effect relationships throughout infancy and childhood. Once captured, these patterns can be integrated in semiphysiologically or physiology-based pharmacokinetic models to support predictions in specific pediatric settings or to support pediatric drug development. Although these translational efforts are crucial, isoenzyme-specific ontogeny-based models should also incorporate data on variability of maturational and nonmaturational covariates (eg, disease, treatment modalities, pharmacogenetics). Therefore, this review provides a summary of the ontogeny of phase I drug-metabolizing enzymes, indicating current knowledge gaps and recent progresses. Furthermore, we tried to illustrate that straightforward translation of isoenzyme-specific ontogeny to predictions does not allow full exploration of scenarios of potential variability related to maturational (non-age-related variability, other isoenzymes or transporters) or nonmaturational (disease, pharmacogenetics) covariates, and necessitates integration in a "systems" concept.