How computational models helped saving a clinical trial during the COVID pandemic

The COVID-19 pandemic disrupted clinical studies globally, including those assessing Ritlecitinib's safety in hepatic and renal impairments. Hindered by recruitment challenges, scientists innovatively employed a computer model to create virtual populations based on previous studies, facilitating the continuation of the clinical trial, even during the pandemic

The COVID-19 pandemic has had a profound and far-reaching impact on our lives, from global economies to the most intimate aspects of our daily routines. Among the many areas affected, clinical studies have been particularly disrupted, with research projects worldwide forced to halt due to widespread lockdown measures.

Clinical studies are critical in assuring safety and efficacy of new drugs, thus transforming from theory to the pharmacy. They facilitate answers to key queries like: is the drug safe? Is it effective? At which dose range? Are there any notable side effects? Etc.

Among the many clinical studies that were affected by the COVID pandemic, two of them were related to the assessment of Ritlecitinib, a drug under development for the treatment of conditions like patchy hair loss (alopecia areata), rheumatic arthritis, Crohn's Disease, and others. Those two studies aimed to determine if Ritlecitinib is safe and tolerable in people with liver (study 1) and kidney (study 2) impairments. Answering such questions is essential since organs like the liver and kidney are responsible for the absorption, metabolism and elimination of many drugs, including Ritlecitinib. Therefore, patients with challenges in liver or kidney function might respond differently to this drug. Understanding this would help define possible implications for the patients and also lead to essential indications in the labelling of the pharmaceutical product.

The above clinical studies aimed at comparison between healthy participants and those with liver impairment (study 1) or kidney impairment (study 2) participants. The "healthy" groups of both studies were intended to be independent. While in the first study it was possible to recruit both healthy participants and patients with liver impairments, in the second one the COVID pandemic abruptly interrupted the recruitment of healthy participants, with the risk of losing years and millions of dollars of investments.

However, the scientists behind this study were determined to try new ways of continuing their study by employing a sophisticated computational model called POPPK, which stands for POPulation PharmacoKinetics, that was able to generate thousands of virtual healthy reference groups based on previous studies.

Ultimately, this approach led to the conclusion that the changes introduced by liver and kidney impairments are not significant enough. However, employing a method based on a computer model marks a relevant antecedent, as it was possible to "recruit" thousands of diverse virtual participants over a large age range and ethnicity. Thus offering more diversity as compared to traditional clinical studies, which many times suffer from limitations in covering the diversity of the human population in terms of age groups and ethnicities.

Overall, the study showed that computer model-based clinical trial simulations can be appropriate for providing robust estimations of the differences between populations when compared to traditional methods of drug development. Moreover, it demonstrated the benefit of safeguarding healthy participants who don’t receive any medical benefit from exposure to medical drugs under development. Such advancements are relevant even beyond the COVID pandemic, allowing for increased safety and faster delivery of new drugs or medical devices.

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Date: 30/05/2024 | Tag: | News: 1584 of 1588
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