Debiopharm

Ulf Andersson

Could you provide an overview of your roles and responsibilities in your previous organizations, and explain how that experience enhances your current role and responsibilities in your current organization?

I serve as the head of the preclinical safety and translational pharmacology team at Debiopharm. This role encompasses two main responsibilities. Firstly, I primarily focus on preclinical safety aspects, ensuring that our compounds are safe for further development. Secondly, I manage the translational pharmacology team, which is responsible for generating preclinical efficacy data through in vitro and in vivo studies. This data plays a crucial role in supporting our submissions and IND applications.

Prior to my position at Debiopharm, I led the safety science team at AstraZeneca, specifically in the Cardiovascular, Renal, and Metabolism (CVRM) disease area. I held this role for three years, which provided me with valuable experience in a different therapeutic area. While Debiopharm mainly focuses on oncology and anti-microbials, CVRM encompassed cardiovascular, vascular, and metabolic diseases. This diverse experience has been rewarding both professionally and personally, as it exposed me to different disease areas and the unique challenges they present in terms of demonstrating efficacy and characterizing molecule toxicity.

Before my time at AstraZeneca, I worked as a toxicologist in various disease areas, including respiratory inflammation, central nervous system (CNS) disorders, and pain management. Throughout my career, I have strived to approach efficacy and safety assessment with a molecular and molecular pharmacological perspective. With my background in molecular biology, I possess a solid understanding of the molecular basis of diseases and toxicity. I have consistently aimed to incorporate a quantitative approach into risk assessment, ensuring a comprehensive understanding of both efficacy and safety profiles.

What are some significant challenges that have recently affected the pharmaceutical industry, particularly in the field of toxicology testing?

One of the recent major impacts in the field is the FDA Modernization Act, which implicitly encourages moving away from animal experimentation. However, this poses a significant challenge as the currently available models for addressing the safety aspects of a molecule primarily focus on a limited number of cell types, such as liver, kidney, and cardiomyocytes, despite there being over 200 different cell types in the body. While it is true that animal toxicology data may not perfectly translate to clinical safety, it still provides valuable information. Estimates suggest that around 50 percent of toxicities can be well predicted from non-clinical studies, making it better than not having any data at all. Therefore, even with the intention to replace some animal experimentation with in vitro testing, it is scientifically justified to continue conducting in vivo toxicology studies to ensure a comprehensive understanding of the molecule's toxicity.

Additionally, there is a growing trend in oncology to explore the use of patient-derived organoids (PDOs) and patient-derived xenograft (PDX) models to replace in vivo testing in preclinical studies. While this is an ongoing trend, scientists working on these projects still feel the need to confirm the findings with non-clinical studies.

Another challenge faced by the industry is the expectation to incorporate artificial intelligence (AI) into strategies to improve translation, clinical efficacy, and safety. Although AI models hold promise and perform well in predicting molecule toxicity and drug combinations, the algorithms they use are often non[1]explainable. This poses a regulatory challenge, as it becomes difficult to justify safety assessments solely based on an AI algorithm without being able to explain the underlying data. While companies are working with AI providers to improve the prediction of clinical efficacy in translational pharmacology, there is still a level of skepticism among scientists who prefer to confirm AI predictions with non-clinical in vivo models, which they have traditionally trusted. AI has not yet demonstrated a significant breakthrough in clinical efficacy and safety that would replace the need for traditional preclinical studies.

What are some technological trends in the pharmaceutical industry that excite you for the future?

I think that AI has a lot to offer, but it should be developed in parallel with advancements in multi-organ, multi[1]microphysiological systems (MPS). These systems hold great potential for predicting the metabolism and fate of drugs and can offer a more comprehensive assessment of safety and efficacy. While many existing MPS models focus on single or dual organs, the development of multi-organ models is an exciting avenue to explore in ensuring the safety and efficacy of molecules in drug metabolism and pharmacokinetics (DMPK). Although some companies have already made significant progress in developing advanced MPS models, it is crucial to validate their predictive capabilities through prospective studies. While retrospective analyses have shown promising results, there are still limited cases of prospective validation where molecules have been optimized and safety assessments have been conducted using MPS models. It is an exciting field, but further validation and optimization are necessary.

This article is based on an interview between Life Science Review Europe and Ulf Andersson.

Another exciting development, particularly in the field of toxicology, is the shift away from relying solely on establishing a no observed adverse effect level (NOAEL) in traditional toxicology studies. Instead, the industry is now focused on employing quantitative risk assessment in conjunction with pharmacokinetic[1]pharmacodynamic (PKPD) modeling and population modeling. This approach allows us to predict the probability of observing an adverse event in a clinical study at a given dose when there is a non-clinical safety signal and represents a significant shift in the pharmaceutical industry. While this approach is less applicable to chemical risk assessment, combining PKPD modeling with toxicology enables a better prediction of clinical outcomes. It is essential that we have a thorough understanding of the molecular basis underlying toxicological findings to develop relevant and robust models for prediction.

What advice would you offer to senior leaders and CXOs working in the industry?

It is indeed crucial for the toxicology field to continue evolving and exploring the potential of in vitro models. We should not solely rely on others to pave the way but actively contribute to the advancement of the science. While there may be some resistance, particularly in smaller biotech and pharma companies, it is important to integrate these models into our safety profiling of molecules.

Moving away from reliance on animal models and towards quantitative risk assessment in toxicology, combined with a deep molecular understanding of toxicity mechanisms, is a valuable direction to pursue. This shift will not only contribute to the evolution of the science but also provide us with increased confidence in the use of microphysiological systems and AI approaches. To enhance our understanding and interpretation of these models, it is crucial to be able to explain the insights they provide. This requires combining advanced computational modeling, such as adverse outcome pathways, with toxicology data and incorporating molecular mechanisms into the analyses.

By merging our understanding of adverse outcome pathways with advanced computational modeling and toxicology data, we can make significant strides in our ability to predict and assess safety outcomes. This integration of different approaches holds promise for enhancing our confidence in microphysiological systems and AI methodologies.