A five-part series exploring the foundational quantitative analysis methodologies that shape clinical pharmacology strategy and pharmacometrics approaches.

At A2-Ai, we believe robust drug development decisions start with asking the right question and a shared understanding of what approaches to take and how the models are built and applied. Modeling Monday is a five-part blog series that highlights the core considerations and modeling methodologies used across drug discovery and development, with a focus of "demystifying" them.

This blog is authored by Rosa Luo, MS, SVP of Clinical Pharmacology, Sean McCann, PhD, Associate Pharmacometrician, and Jeff Clarine, MS, VP of DMPK, who bring complementary perspectives from clinical pharmacology and pharmacometrics. Together, they will walk through commonly used quantitative analysis and modeling techniques, when they are most impactful, and how they inform real-world decision making.

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic, in silico approach that integrates drug-specific properties with age-dependent human physiology to predict drug exposure in virtual patient populations, making it a valuable tool for optimizing dose selection and study design when clinical data are limited, as is often the case in pediatric development.

Challenges in Pediatric Dose Selection

Pediatric dose scaling is critically important because children are a vulnerable population and exposing them to drug levels that are subtherapeutic or unnecessarily high can compromise both efficacy and safety. Additionally, design of clinical pediatric studies is constrained by ethical requirements that limit blood sampling frequency and volume, the practical challenges of enrolling children across a wide range of developmental ages, and the need to minimize exposure to experimental treatments.

For background or a deeper dive into these principles, see references: Miller, 2019 (PBPKBasics), Verscheijden, 2020, Yates, 2026, and Zhou, 2022 (PBPK in Pediatric Populations), and Calvier, 2017 (Allometric Scaling in Pediatrics).

Why Children Are Not Small Adults

Simple allometric scaling falls short of effective dose estimation for pediatric populations as, physiologically speaking, children are not “small adults”.Allometry alone fails to incorporate developmental differences between pediatric patients and adult patients. The FDA guidelines (General Clinical Pharmacology Considerations for Pediatric Studies of Drugs, Including Biological Products Guidance for Industry, September 2022) divide the pediatric population into four sub-populations:

• Neonates (birth to 1 month)
• Infants (1 month to 2 years)
• Children (2 to 12 years)
• Adolescents (12 to under 17 years)

Within each of these pediatric sub-populations differences exist that directly impact the absorption, distribution, metabolism, and excretion (ADME) of a drug. Based on these differences within the pediatric population and against the adult population it is clear why simple body weight or body surface area (BSA) scaling is insufficient for first-in-pediatric dose selection especially for drugs with narrow therapeutic indices. Also, given the diversity of the ADME related properties that drive disposition of a drug in age specific groups, inclusion of all age ranges is often impractical in clinical trial study designs.

PBPK Modeling for Pediatric Dose Estimation

The FDA’s guidance for considerations on pediatric studies (September, 2022), highlights PBPK modeling as a viable in‑silico approach for pediatric dose estimation because PBPK models do the following:

• Integrate drug‑dependent parameters such as physicochemical properties, intrinsic clearance, and interactions with enzymes, transporters, and plasma proteins.
• Incorporate system‑ and age‑dependent physiology, including blood flow, protein levels, organ size and composition, and developmental patterns of enzymes and transporters.

The combination of these key properties in a PBPK model account for the developmental differences that influence pediatric drug exposure. In these models, virtual populations defined by age include developmental physiological differences across the pediatric populations.Those differences, when combined with a validated adult PBPK model and an understanding of the drug’s PK/PD relationship, enable developers to design pediatric studies and select starting doses in children that are both effective and safe.

Applications of PBPK Modeling in Pediatric Clinical Development

This approach supports the following examples in pediatric clinical study designs:

1. Dose selection for first-in-pediatric studies: To inform the starting dose in pediatric studies, a pediatric PBPK model can simulate the expected C_max and AUC in age specific cohorts for a given dose.Based on these simulations, starting doses for first-in-pediatric studies can be identified that will both reduce the number of dose escalation steps required and reduce the risk of underexposure or overexposure to pediatric patients (Macente, 2025 and Cheung, 2024).
2. PK sampling and bridging PK data: For both ethical and practical reasoning, blood sampling in children is more limited in pediatric patients than adult patients (ICH E11A, 2024 and FDA, 2022). Therefore, sparse and opportunistic sampling is often implemented (especially in neonates and infants) in clinical study designs. PBPK models can both optimize selection of when to perform blood sampling (Autmizguine (2014))and in combination with population PK models built on sparse data can fill gaps (e.g. complete thePK profile between 1 and 8 h if PK data points only exist at 1 and 8 h in pediatric patients) in the PK curves without requiring dense sampling (Cleary, 2026).
3. Addressing formulation differences between pediatric and adult patients: When an approved drug in adults requires a new age-appropriate formulation for pediatric use (e.g oral liquid, dispersible tablet, or modified-release dosage form) sponsors cannot assume bioequivalence from adults to children due to age-related changes in GI tract. By incorporating the physiological differences of the pediatric GI tract, specific properties of the formulation, and physiochemical properties of the drug, PBPK models can simulate exposure equivalence between children and adults. This approach was accepted by both the FDA and EMA for a pediatric tofacitinib formulation where bioequivalence was virtually demonstrated in a validated PBPK model between an immediate-release oral solution dosed BID (developed to allow weight-based dosing in children) and a modified-release microsphere formulation dosed QD (Wu, 2025).

Conclusion:

In conclusion, pediatric PBPK models strengthen confidence in proposed first‑in‑pediatric doses, enable exploration of study designs that safely and efficiently escalate to therapeutic exposure ranges, optimize selection of PK timepoints, allow sparse PK data to be bridged to full PK profiles. Additionally, they can be used to explore PK and support registration of pediatric specific formulations.

References:

ICH E11A: Pediatric Extrapolation — Guidance for Industry (FDA, December 2024).

FDA Draft Guidance: General Clinical Pharmacology Considerations for Pediatric Studies of Drugs, Including Biological Products (September 2022).

Autmizguine J, Benjamin DK Jr, Smith PB, Sampson M, Ovetchkine P, Cohen-Wolkowiez M, Watt KM. Pharmacokinetic studies in infants using minimal-risk study designs. Curr Clin Pharmacol. 2014;9(4):350-8. doi: 10.2174/1574884709666140520153308. PMID: 24844642; PMCID: PMC4703884.

Calvier EA, Krekels EH, Välitalo PA, Rostami-Hodjegan A, Tibboel D, Danhof M, Knibbe CA. Allometric Scaling of Clearance in Paediatric Patients: When Does the Magic of 0.75 Fade? Clin Pharmacokinet. 2017 Mar;56(3):273-285. doi: 10.1007/s40262-016-0436-x. PMID: 27510367; PMCID: PMC5315734.

Cheung SYA, Hay JL, Lin YW, de Greef R, Bullock J. Pediatric oncology drug development and dosage optimization. Front Oncol. 2024 Jan 29;13:1235947. doi: 10.3389/fonc.2023.1235947. PMID: 38348118; PMCID: PMC10860405.

Cleary Y, Prasad B, Ogungbenro K, Gertz M, Galetin A. Population Physiologically-Based Pharmacokinetic Modeling to Determine Ontogeny: A Quantitative Clinical Pharmacology Example in Pediatric Rare Disease. CPT Pharmacometrics Syst Pharmacol. 2026 Feb;15(2):e70174. doi: 10.1002/psp4.70174. PMID: 41607354; PMCID: PMC12853142.

Macente J, Hernandes Bonan R, Caleffi-Marchesini ER, Leira Pereira LR, De Freitas Lima P, Annaert P, Allegaert K, Diniz A. Physiologically-based pharmacokinetic modeling and simulation for initial dose optimization of levetiracetam in pediatrics. Front Pharmacol. 2025 Dec 4;16:1678960. doi: 10.3389/fphar.2025.1678960. PMID: 41424790; PMCID: PMC12711802.

Miller NA, Reddy MB, Heikkinen AT, Lukacova V, Parrott N. Physiologically Based Pharmacokinetic Modelling for First-In-Human Predictions: An Updated Model Building Strategy Illustrated with Challenging Industry Case Studies. Clin Pharmacokinet. 2019 Jun;58(6):727-746. doi: 10.1007/s40262-019-00741-9. PMID: 30729397.

Verscheijden LFM, Koenderink JB, Johnson TN, de Wildt SN, Russel FGM. Physiologically-based pharmacokinetic models for children: Starting to reach maturation? Pharmacol Ther. 2020 Jul;211:107541. doi: 10.1016/j.pharmthera.2020.107541. Epub 2020 Apr 1. PMID: 32246949.

Wu F, Tsakalozou E, Burckart GJ, Žakelj R, Gaohua L, Sagawa K, Lukacova V, Vaithiyalingam S, Fan J, Fotaki N, Patel N, Fang L. PBPK Modeling to Support Bioavailability and Bioequivalence Assessment in Pediatric Populations. Pharm Res. 2025 May;42(5):847-855. doi: 10.1007/s11095-025-03846-y. Epub 2025 Mar 26. PMID: 40140126; PMCID: PMC12158840.

Yates JWT, Zientek M, Taskar KS, Lin W, Heimbach T, Willmann S, Rehmel J, Parrott N, Hanley M, Badee J, Chen Y, Cole S, De Zwart L, Haertter S, Jiang R, Kotsuma M, Liang G, Lin YW, Liu J, Ou Y, Rascher J, Shaik NA, Wahlstrom J, Wang X, Xiao G, Yee KL, Cheung SYA. Physiologically-Based Pharmacokinetic Modeling to Support Pediatric Clinical Development: An IQ Working Group Perspective on the Current Status and Challenges. CPT Pharmacometrics Syst Pharmacol. 2026 Jan;15(1):e70141. doi: 10.1002/psp4.70141. Epub 2025 Nov 18. PMID: 41255069; PMCID: PMC12823307.

Zhou X, Dun J, Chen X, Xiang B, Dang Y, Cao D. Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools. CPT Pharmacometrics Syst Pharmacol. 2023 Jan;12(1):13-26. doi: 10.1002/psp4.12883. Epub 2022 Nov 20. PMID: 36330677; PMCID: PMC9835135.