Simulated Patients Replace Human Trials in Virtual Test of Roxithromycin Drug Equivalence

Generic medicines are supposed to behave in the body exactly like the original branded products they replace, and for decades the only accepted way to prove that has been the bioequivalence study: a carefully monitored clinical trial in which healthy volunteers swallow both formulations on separate occasions while researchers draw blood at regular intervals and compare the resulting drug concentration profiles. A new study from a team at the Affiliated Hospital of Guizhou Medical University in China, published in BMC Pharmacology and Toxicology, demonstrates a strikingly different route to the same conclusion. Rather than recruiting human subjects, the researchers built a computer model of how the antibiotic roxithromycin travels through the human body, validated that model against real clinical data, and then used it to run a fully virtual bioequivalence trial in a simulated population of ninety-nine East Asian adults. The results suggest that the two formulations in question deliver therapeutically interchangeable drug exposure, and that virtual trials of this kind can reproduce the regulatory criteria normally applied to studies in people.

The drug at the center of the study, roxithromycin, is a macrolide antibiotic related to erythromycin, used against respiratory tract and other bacterial infections. Like many macrolides, it is given orally and its absorption into the bloodstream depends on a chain of events that includes dissolution of the tablet in the gastrointestinal tract, permeation across the intestinal wall, and distribution into tissues. Any change in the formulation, the manufacturing process, or even the excipients used to bind and coat the tablet can in principle alter how quickly and completely the drug reaches the circulation. That is why regulators require bioequivalence testing whenever a generic version or a modified formulation is brought to market. The standard metrics are the area under the concentration-time curve, which reflects total drug exposure, the maximum plasma concentration, which reflects peak exposure, and the time at which that maximum occurs. If the ratios of these parameters between test and reference products fall within a ninety percent confidence interval of eighty to one hundred twenty-five percent, the products are considered bioequivalent.

Conducting such trials in humans is expensive, slow, and ethically burdensome, since volunteers are exposed to a drug without any personal therapeutic benefit. This is where physiologically based pharmacokinetic modeling, or PBPK, enters the picture. Unlike simpler empirical models that treat the body as a set of mathematical compartments with fitted parameters, a PBPK model attempts to represent the actual anatomy and physiology of the body: organs and tissues are connected by blood flows, and drug movement between them is governed by physicochemical properties such as solubility, permeability, lipophilicity, and protein binding, together with physiological parameters like organ volumes, blood flow rates, and enzyme abundances. Because the underlying structure reflects real biology, a well-constructed PBPK model can be adapted to different populations, doses, and even formulations simply by swapping in the relevant inputs, making it a powerful tool for predicting drug behavior in scenarios that have never been tested clinically.

The research team, led by Danfeng Yu, Qin Li, and colleagues including corresponding authors Jiyu Chen and Yan He, assembled roxithromycin’s physicochemical, in vitro, and in vivo parameters from established databases and the published literature, and constructed an adult PBPK model using the open-source software platform PK-Sim. The initial model was developed and calibrated against clinical pharmacokinetic data from a 150 milligram dose of the reference formulation. The critical test of any model, however, is not how well it fits the data used to build it, but how well it predicts data it has never seen. To address this, the researchers performed a comparative evaluation using clinical data from the test formulation, checking whether the model could reproduce concentration-time profiles for a product whose behavior had not informed the model’s construction.

To quantify the agreement between prediction and observation, the team applied the mean fold error approach, a standard metric in pharmacokinetic modeling that measures the ratio of predicted to observed values. For the three key parameters, the area under the curve from zero to the last measurable time point, the maximum concentration, and the time to maximum concentration, more than ninety percent of the mean fold error ratios fell within the range of 0.5 to 2. This means that for the overwhelming majority of comparisons, the model’s predictions were within a factor of two of the measured values, and in most cases considerably closer. With the model thus validated, the researchers imported formulation-specific in vitro dissolution data for both products, allowing the simulations to distinguish between the two tablets based on how rapidly each released its drug payload in the digestive tract.

The virtual bioequivalence trial itself was conducted in a simulated cohort of ninety-nine East Asian subjects, a population chosen to match the demographic context in which the formulations are intended to be used. Virtual subjects in PBPK platforms are not identical clones; the software samples from realistic distributions of physiological parameters such as body weight, gastric emptying time, and intestinal transit, so that the simulated population exhibits the same variability that a real trial population would. Each virtual subject received both formulations, and the resulting pharmacokinetic profiles were analyzed exactly as they would be in a conventional bioequivalence study. The outcome was unambiguous: the ninety percent confidence intervals for the geometric mean ratios of the primary pharmacokinetic parameters all fell within the regulatory acceptance range of eighty to one hundred twenty-five percent, indicating that the two formulations would be judged bioequivalent.

The implications of this result extend well beyond a single antibiotic. Bioequivalence studies are among the most numerous clinical trials conducted worldwide, required for every generic approval and for many post-approval manufacturing changes. If validated PBPK models can reliably substitute for some of these trials, the savings in cost, time, and human exposure could be substantial. Regulators, including the United States Food and Drug Administration and the European Medicines Agency, have already issued guidance on when modeling and simulation can support or replace certain clinical studies, and virtual bioequivalence is an active area of regulatory science. The present study adds to a growing body of evidence that, for drugs with well-characterized pharmacokinetics and formulations whose dissolution behavior can be measured reliably in vitro, a computer can indeed stand in for the clinic.

The study also illustrates the methodological discipline that makes such virtual trials credible. The researchers did not simply assume the model was correct; they built it from one dataset, tested it against an independent dataset from a different formulation, and reported a formal quantitative measure of predictive performance before trusting it for the bioequivalence question. The work was registered retrospectively at ClinicalTrials.gov under number NCT06798051, the underlying clinical study was approved by the Medical Ethics Committee of the Affiliated Hospital of Guizhou Medical University, and all participants in the source trials provided informed consent. The modeling was funded by the Science and Technology Planning Project of Guizhou Province and related institutional programs, and the authors declared no competing interests.

Certain caveats remain. A virtual trial is only as good as its underlying model, and PBPK predictions for drugs with complex metabolism, food effects, or narrow therapeutic windows demand even more extensive validation than was possible here. The ninety-nine subject cohort, while adequate for this comparison, is a simulation of a specific population rather than a demonstration across all patient groups. Nevertheless, the study offers a concrete template for how virtual bioequivalence can be conducted and documented: assemble the drug’s parameters, build and validate the model against real data, import formulation-specific dissolution profiles, and simulate a demographically realistic cohort under the same statistical criteria that govern human studies. As more models of this kind accumulate and are independently verified, the day when a generic drug can win approval on the strength of a computer trial, with human studies reserved for cases the models cannot yet handle, moves measurably closer.

Subject of Research: Virtual bioequivalence assessment of two roxithromycin formulations using a physiologically based pharmacokinetic model

Article Title: Virtual bioequivalence assessment for two roxithromycin formulations using a physiologically based pharmacokinetic model

Article References: Yu, D., Li, Q., Zeng, L., Liao, M., Lu, X., Chen, J., & He, Y. (2026). Virtual bioequivalence assessment for two roxithromycin formulations using a physiologically based pharmacokinetic model. BMC Pharmacology and Toxicology. https://doi.org/10.1186/s40360-026-01244-7

Image Credits: AI Generated

DOI: 10.1186/s40360-026-01244-7

Keywords: roxithromycin, PBPK modeling, virtual bioequivalence, PK-Sim, pharmacokinetics, generic drugs, drug dissolution, macrolide antibiotics, clinical trial simulation, regulatory science, East Asian population, BMC Pharmacology and Toxicology