At home, this may mean relying on AI to help us answer day-to-day questions. I’ve used it to plan summer vacations, help design our new interior paint colors, and help with the kids’ homework (yes, we all do it!).

At work, the gaps between users grow. For some, using AI means having a chatbot help write emails and summarize meetings, while for others, it’s building custom agents, automating workflows, or generating in minutes what might otherwise take hours to build manually in Excel or other programs. Adding to the disparate meanings is the fact that in the pharmaceutical and biotech world, “AI” has been used interchangeably with terms like machine learning models, translational insights, digital twins, or advanced simulation environments.

None of these perspectives are wrong, they simply reflect where each person sits within a rapidly evolving AI ecosystem. However, with growing pressure on individuals and teams alike to expand AI skills, lacking a shared definition creates a risk of building disconnected capabilities rather than connected intelligence.

I have seen this over the last decade or more with companies looking to upskill in their modeling and simulation platforms to support translation discovery and clinical research. It requires a coordinated effort to allow teams to work together to implement the best solution for their company. Similarly, everyone is now racing to upskill in AI and develop their own interpretation of what that means without always considering how those technologies communicate, integrate, or support the wider scientific process within their companies.

That seems like a missed opportunity.

Right now, organizations have the chance to incorporate AI in an ecosystem framework, where technologies, data, scientific models, and people work together seamlessly. Efficiency in our industry comes from creating environments where information flows freely, where insights can be trusted across teams and disciplines, and where AI enhances decision-making rather than complicating it.

However, to make this a reality, leaders must first create and communicate a shared vision of the purpose and goals of AI use within their organizations. Otherwise, they will continue to be plagued by inconsistent and duplicative applications and agents, running up unnecessary costs without realizing the full benefit their technologies can offer.

Here is my vision for your consideration, a vision that is shared by all of us at Simulations Plus: I believe the future of AI in modeling and simulation will be defined by connectivity and interoperability. We need platforms and ecosystems that bring together mechanistic science, simulation technologies, data-driven AI, and human expertise into a coherent framework.

AI should not sit outside the scientific process as a separate layer of technology. It should exist within it, acting as a co-scientist, supporting and enabling better decisions across discovery, development, and clinical research.

The organizations that succeed over the next few years will not simply be the ones with the largest datasets who are adopting AI the fastest. They will be the ones that build connected ecosystems capable of turning complex science into faster, more confident decision-making and ultimately delivering better therapies to patients in a reliable and trusted way.

If you are interested in learning more about how to implement an AI-powered ecosystem, I’m happy to chat.

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The expiration of the HSR waiting period satisfies one of the regulatory conditions necessary for the completion of the transaction. The transaction remains subject to the satisfaction or waiver of other customary closing conditions, set forth in the merger agreement, including approval by Simulations Plus shareholders and receipt of certain regulatory approvals in France. Subject to the satisfaction or waiver of the remaining closing conditions, the transaction is currently expected to close in the second half of calendar 2026.

That trust is not driven by any single output or diagnostic, however; it emerges from the consistency between the modeling objective, the data, the assumptions, and the conclusions.

There are key elements that help form that consistency and build regulatory trust. This blog post details five that are critical to consider as you plan and implement your development program.

1. Clear alignment with the regulatory question
   

   The model is explicitly designed to address the decision of interest (e.g., dose selection, labeling), with a logical connection between objectives, data, and conclusions.

2. Validation aligned with model risk and impact
   

   The extent of model evaluation reflects its role in decision-making; high-impact models require more rigorous testing, including sensitivity analyses and alternative scenarios.

3. Reproducible workflows
   

   Model code, datasets, and execution steps are sufficiently documented to allow independent re-analysis without ambiguity or manual intervention.

4. Transparent assumptions and limitations
   

   Structural choices, covariate relationships, and data handling decisions are clearly justified, with an explicit discussion of their potential impact on conclusions.

5. Consistency between diagnostics and conclusions
   

   Goodness-of-fit, predictive checks, and other diagnostics support the stated conclusions, with no unresolved discrepancies. Ultimately, regulatory confidence is built when a model is not only technically sound, but understandable, reproducible, and clearly connected to the decision it informs.

Transparency, interpretability, and validation are not independent concepts. They work together to establish trust. When aligned, they enable models to serve their intended role: supporting decisions that impact patient safety and therapeutic outcomes.

If you’d like to enhance trust in your program, schedule a 15-minute call with our regulatory experts to learn where you can optimize your efforts.

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Health authorities worldwide require clinical studies in children to ensure scientifically rigorous and consistent dosing. Before pediatric data are available, model-based methods provide a scientific and reproducible way to determine doses for pediatric studies. A general introduction to pediatric scaling is provided, followed by a focus on population pharmacokinetic methods to scale from adults to children using exposure matching. Exposure matching involves two steps: first, optimal pediatric doses are derived using a fine grid of body sizes and doses; second, feasible doses are selected based on availability of dose strengths. Three strategies are employed and compared, best fit (similar exposure in adults and children across ages and body sizes), conservative (children’s exposure not exceeding adults’), and progressive (children’s exposure not less than adults’). Each recommendation is evaluated against the exposure metrics Cmax, Ctrough, and AUC at steady state for the optimal dosing scheme. Simulations assess interindividual variability in exposure. Visualizations enable risk assessment of exposure distributions and post-hoc refinement of dosing schemes. The formalized approach offers clinical teams a reproducible basis for pediatric dose selection. An implementation in R and Monolix is provided.

By Andreas Krause & Géraldine Cellière

In this blog post, we’ll share six issues that are among the most common sources of regulatory skepticism.

Poor traceability or undocumented data transformations

When reviewers cannot clearly trace how the analysis dataset was derived from source data, confidence in the entire analysis is undermined. Missing documentation for data cleaning, filtering, or variable derivation creates ambiguity and prevents independent evaluation. Even minor inconsistencies, such as unexplained exclusions or time variable misalignment, can trigger extensive follow-up questions and delay review.

Lack of transparency in handling BLQ data, outliers, or missing values

Ad hoc or poorly justified approaches to data issues are a frequent source of regulatory concern. For example, excluding outliers without justification, applying inappropriate BLQ methods, or ignoring missing data mechanisms can introduce bias. Reviewers expect clear documentation of these decisions, along with sensitivity analyses demonstrating that conclusions are not driven by arbitrary choices.

Something to remember, most of these issues do not stem from what would appear to be “incorrect” modeling techniques, but from insufficient transparency and justification. In practice, reviewers may lose trust in models not because they may be wrong, but because the reviewer cannot confidently determine whether they are right.

Inadequate sensitivity analyses for key assumptions

High-impact assumptions, such as handling of BLQ data, residual error structures, extrapolation beyond the available data, or included mechanisms of action must be stress-tested. Failure to explore how alternative assumptions affect conclusions raises concerns about robustness. Sensitivity analyses are particularly important for models informing dosing or labeling decisions, where small changes can have meaningful clinical implications.

Unjustified model structure or overly complex models without added value

Models that are overly complex without clear improvement in predictive performance or interpretability raise concerns about overfitting and lack of parsimony. Similarly, structural assumptions (e.g., compartmental models, mechanistic components) must be justified based on prior knowledge or biological rationale. Without this, reviewers may question whether the model reflects true system behavior or is simply tailored to the dataset.

Inconsistent or incomplete diagnostics

Providing standard diagnostics (e.g., goodness-of-fit plots, Visual Predictive Checks, VPCs) is necessary but not sufficient. Trust is reduced when diagnostics are incomplete, poorly labeled, or inconsistent with reported conclusions. For example, claiming adequate model performance while visual predictive checks show systematic bias will immediately raise concerns. Diagnostics must not only be presented but also interpreted in a way that aligns with the overall narrative.

Over-reliance on statistical significance without clinical interpretation

Statistical criteria (e.g., p-values, likelihood ratio tests) should not be the sole basis for model decisions or interpretation of model results. As an example, covariates might be included based on statistical criteria. However, regulatory reviewers will expect covariate effects to be clinically meaningful and biologically plausible. Models that include statistically significant but clinically irrelevant covariates risk being viewed as unstable or non-generalizable, especially in extrapolation scenarios.

It is critical to understand regulatory perspectives and build your development strategy to avoid these and other potential issues around model trustworthiness. Schedule a 15-minute call with one of our regulatory experts to learn how we can support your program.

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Glyoxylic acid (GA), the active ingredient of heat-activated hair straightening products (GHSPs) has been discussed in the context of acute kidney injury (AKI) via oxalic acid (OA) crystal formation and of heat-decomposition resulting in release of formaldehyde. Assessment of dermal absorption following realistic GHSP treatments with two marketed products resulted in maximally 7.73 μg/cm² of bioavailable GA/application. Considering the scalp area of 580 cm², the systemic GA exposure of consumers was 4.48 mg, equivalent to 1.1 mg OA taking into account that 25% GA is dermally metabolized to OA. Assessment of GA exposure to stylists’ hands indicated systemic OA levels in the range of 0.026 mg/day (assuming 3 treatments in one day). Compared to typical dietary and endogenous sources of OA of 22–45 mg/day, GHSP-dependent OA exposures of consumers and stylists were 20–41-fold and > 860-fold lower, respectively. PBK prediction of GHSP-dependent renal OA excretion of consumers was <1.27 mg/day compared with normal excretion of ∼25 mg/day and the 40 mg/day threshold associated with AKI. Predicted kidney OA concentrations during GHSP exposure further indicated that OA crystal formation is unlikely to occur. Assessment of the release of the GA heat-decomposition byproduct formaldehyde in the ambient air during GHSP treatment was <25 μg/m³ and below regulatory exposure limits e.g., 100 μg/m³ (WHO indoor air quality limit). In conclusion, quantification of GA-dependent formaldehyde and OA exposure indicates no health concerns for consumers and stylists under the described realistic GHSP treatment conditions.

By Nicola J. Hewitt, Carsten Goebel, Jens Diller, Anne Fuchs, Rolf Fautz, Brunhilde Blömeke, Katharina Schwarz, Shimpei Terasaka, Kazutoshi Saito, Rebecca Justiniano, Gábor von Bölcsházy

The biocompatible macromolecules play a pivotal role in enhancing the performance of various drug delivery systems. In the current work, cellulose acetate (Opadry® CA), microcrystalline cellulose and an intrinsically uncharged polymer, polyethylene oxide (Polyox WSR N 80 and Polyox WSR 303), were evaluated for developing a bilayer push-pull osmotic tablet for dexibuprofen. The drug layer was comprised of Polyox WSR N 80, osmogen, microcrystalline cellulose and magnesium stearate. The push layer consisted of Polyox WSR 303 and pigments (Fe 2O3) in addition to osmogen and lubricant. The optimum levels of the input variables, osmogen (sodium chloride), orifice size, and percent coating weight gain were determined using the simultaneous multivariate techniques of Artificial Neural Network (ANN) and Central Composite Design (CCD) to achieve the targeted drug release profile. The ANN model’s prediction profiler was cross-validated using the CCD-optimized formulation. The tabletability of the poorly compressible drug, dexibuprofen, was improved by incorporating microcrystalline cellulose as a diluent. The ANN- and CCD-driven trial and optimized formulations were tested for both critical and pharmaceutical quality attributes. Additionally, the optimized formulations FANN was also tested for thermal and chemical stability, physicochemical interactions, and surface morphology. Physiologically Based Pharmacokinetic (PBPK) models for optimized formulations were developed using GastroPlusTM to simulate in vivo plasma profiles and compare with the real-time human pharmacokinetic data. The study demonstrates the promising role of biocompatible macromolecules in compressing the poorly compressible molecule, dexibuprofen, and modulating its release and in vivo pharmacokinetic performance from an osmotically controlled push-pull system for 12 h.

By Nazish Mumtaz, Rabia Ismail Yousuf, Muhammad Harris Shoaib, Kamran Ahmed, Muhammad Talha Saleem, Fahad Siddiqui, Sadaf Farooqi, Muhammad Suleman Imtiaz