Drs. Zhu and Booth review some of the recent approaches and data collection to select doses, including randomized dose cohorts, before segueing into the next big hurdle in dose selection, that being dose selection of combination regimens. Questions about how to select the dose of new therapeutic added to another approved agent, to established regimens with multiple agents, and two novel therapeutics are discussed.

Model-informed drug development (MIDD) has been an invaluable set of tools, which is playing an increasingly important role for oncology dose selection, especially for novel modalities such as monoclonal antibodies, bispecific antibodies, antibody-drug-conjugates, oligonucleotides, or cells. MIDD tools have been invaluable for the exploration of the treatment of various cancers. These novel modalities are associated with unique mechanisms of actions and pharmacology features. It has been shown that modeling and simulation tools that are developed based on small molecules can be readily applied to support new drug development for these novel modalities. The focus of the modeling and simulation work may differ to reflect the unique issues in the development programs for these novel modalities. Drs. Zhu and Booth also share some of the modeling work that has been conducted for dose selection of novel modalities as case examples.

This blog post is the first installment of a series outlining relevant topics such as practical principles, common pitfalls, and review-driven insights that extend beyond formal guidelines, focusing on what drives reviewer confidence in a modeling approach. In this post, we’ll focus on model transparency.

What Model Transparency Means in Practice

Transparency enables reviewers to understand the model in the context of use, including its structure, assumptions, constraints, how it supports the submission, to independently verify results, and evaluate how these elements influence conclusions. In practice, this level of transparency is achieved through a set of core, interdependent elements that ensure traceability, reproducibility, and scientific rigor throughout the modeling workflow. Transparency is not a single action, but is built through multiple deliberate practices across the modeling lifecycle, including:

1. Explicit linkage between modeling objective and regulatory decision (i.e., context of use)
2. Clear description of model structure, assumptions, constrains, and covariate relationships with scientific justification
3. Traceable parameter values and datasets with documented sources and derivations
4. Executable model code and defined software environments
5. Scientifically justified assumptions and covariate relationships
6. Diagnostics aligned with conclusions
7. Clear communication of uncertainty and limitations

What Documentation Do Regulators Expect?

Regulators expect submissions to provide a complete, reproducible, and decision-focused package. While guidance documents outline requirements, review practice consistently emphasizes clarity, traceability, and reproducibility. To meet these expectations, sponsors must ensure that key components of the modeling workflow are clearly documented and aligned with the decision being supported, including:

1. Clear question of interest linked to the regulatory decision (e.g., dose selection, labeling)
2. Final analysis parameter values and/or datasets with full traceability and documented derivations
3. Model files, scripts, software versions, and instructions
4. Diagnostics package consistent with agency expectations
5. Justified handling of BLQ data, outliers, and missingness with sensitivity analyses
6. Clinically meaningful covariate evaluation
7. Simulations that directly support conclusions and reflect uncertainty
8. Explicit discussion of limitations and their impact on decisions

Transparency is not merely a matter of providing complete information; it is about clearly exposing the scientific logic and assumptions that underly a model. True transparency allows others to understand how conclusions are reached, to rigorously test those conclusions, and to judge their credibility with confidence. Without this level of visibility, even a well-documented model can fall short of being trustworthy.

In the next article, we turn to the context in which interpretability matters most. Specifically, where models play a direct role in shaping regulatory decisions. It is at these critical junctures that the need for clear, defensible, and fully interpretable modeling becomes paramount.

Background/Objectives: Propranolol (PROP) is a non-selective β-blocker widely prescribed for cardiovascular and neurological disorders. Its pharmacokinetics (PK) are highly variable, and co-administration with omeprazole (OME), a CYP2C19 substrate and inhibitor, may alter systemic exposure. Herein, this study aimed to investigate factors influencing PROP PK variability and evaluate the effect of OME coadministration using physiologically based pharmacokinetic (PBPK) modeling and population PK (popPK) analysis.

Methods: PBPK models for PROP and OME were developed and validated against published data. DDI simulations were conducted across clinically relevant dosing regimens. A two-period fixed-sequence virtual trial of 125 subjects was simulated with PROP alone and PROP combined with OME. Population PK (popPK) analysis was performed on simulated plasma concentration data to identify covariates affecting PROP disposition and quantify DDI magnitude.

Results: PBPK models were successfully developed and validated. PROP disposition was best described by a two-compartment model with linear elimination. Health status was found to influence clearance, and body surface area (BSA) affected the central volume of distribution. Co-administration with OME increased PROP exposure, with larger effects in patients with renal impairment. Simulated plasma concentrations remained below established toxicity thresholds.

Conclusions: Virtual clinical trials integrating PBPK and popPK modeling provide a robust approach to identifying key determinants of PK variability and DDI risk. Although these findings were not directly translated to clinical observations, this helps identify sources of PK variability in PROP treatment settings and factors that may intensify its interaction with OME, thereby supporting model-informed precision dosing to enhance safety and efficacy.

By Lara Marques and Nuno Vale

Simulations Plus, Inc. (Nasdaq: SLP) (“Simulations Plus” or the “Company”), a global leader in model-informed and AI-accelerated drug development that advances biopharma innovation, today announced that Shawn O’Connor, Chief Executive Officer, will be participating in the 23rd Annual Craig-Hallum Institutional Investor Conference taking place in Minneapolis, Minnesota. Mr. O’Connor will host one-on-one meetings with institutional investors on Thursday, May 28, 2026.

For more information about the events or questions about registration, interested parties should reach out to their contacts at Craig-Hallum.

• Systemic lupus erythematosus (SLE) trials suffer from equivocal or failed trial outcomes.
• The objective of this work is to use a quantitative systems pharmacology (QSP) model to establish an analysis framework that characterizes trial outcome trade-offs stemming from disease heterogeneity and entry criteria.
• This framework aims to improve prediction of treatment response to inform trial design and precision medicine.

Nintedanib (Nint), a tyrosine kinase inhibitor, is approved by the FDA for the treatment of idiopathic pulmonary fibrosis (IPF) by administration as an oral tablet. However, the oral bioavailability of Nint is extremely poor (<5%) due to minimal gastrointestinal absorption, P-gp-mediated efflux, which reduces drug uptake, and extensive first-pass hepatic metabolism. Along with high fecal excretion (93.4%), oral Nint also shows different adverse effects like nausea, vomiting, abdominal pain, and diarrhea, primarily due to the high dose requirement to achieve optimal therapeutic efficacy. To address these limitations, localized, non-invasive delivery of Nint by inhalation has been extensively investigated. Inhaled delivery can be achieved by either liquid (nebulized, metered dose inhaler) or as a solid (dry powder inhalation (DPI) drug product. Although many published studies on inhaled nintedanib highlight superior in vitro and preclinical data, few studies explore the clinical translation of inhaled Nint drug products. Here, we focus on developing a physiologically based pharmacokinetic (PBPK) model to predict the systemic and regional pharmacokinetic (PK) parameters of inhaled Nint when administered for a DPI drug product, using GastroPlus 9.9.

By Zia Uddin Masum & Vivek Gupta