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Biosimulation: Journey Through the Therapeutic Life Cycle
Modeling can support critical decisions across the therapeutic lifecycle, sustaining target progression through to approval.!--h2>
Pharmaceutical companies’ investments in biosimulation are growing by more than 18% annually, and for good reason. With innovation, regulatory, and budget pressures, there is a need to move to a rational drug development process built on deeper knowledge and lower costs.
Simultaneously, our growing awareness of the complexity of disease and the volume of collected data is challenging pharmaceutical companies who must extract the most efficacious and lowest risk therapies from complex, uncertain, and variable data.
Modeling technologies and techniques are evolving from PK/PD models to larger physiological mechanistic models that can represent virtual patients and patient populations with clinical outcomes. These full disease and population simulations allow scientists as early as target identification to gain quantitative clinical insights into safety and efficacy many years ahead of clinical trials.
Physiological mechanistic models range from smaller targeted models to answer specific R&D questions to whole-disease models that represent the complete pathophysiology of a specific disease (Figure 1). With the increasing size and complexity of any model, the opportunity arises for therapeutic groups to improve their understanding and insights into the variability and uncertainty characteristic of living systems and populations. Importantly, as pharmaceutical companies focus on precision medicines to deliver highly efficacious low-risk therapeutics for subpopulations, these modeling approaches open new opportunities for improving our understanding of population uncertainty and variability for success.
A strategic investment in biosimulation can support critical decisions across the therapeutic lifecycle sustaining target progression through to approval. The role of physiological modeling changes from early research to the clinic. During early stages of research, physiological simulation minimizes risk and supports a “fail early” paradigm. Later in development, simulation supports the fast tracking of success by helping to stratify patients and therapies for targeted treatment approaches.
The full value of physiological mechanistic modeling across R&D can only be achieved with a complete representation of the pathophysiology of the disease (Figure 2) and by representing the diversity of the targeted disease population. Using a complete quantitative simulation of the disease that represents the diseased population, scientists can glean a more accurate view into the disease, therapeutic effects, and market opportunities.
Disease Models, Population Insights
Therapeutic groups that invest in more complete disease models versus smaller, more tactical targeted models derive greatest benefit. The greater depth and breadth of the pathway gives scientists an improved quantitative understanding of redundancies, feedback loops, and potential adverse events. The result is a deeper understanding of the pathophysiology of the disease, the proposed intervention, and thereby a better understanding of the likely clinical response and risks.
The depth and breadth of mature physiological models offer opportunities to understand the complexity of a disease in a simulated individual and enable scientists to perturb the model to explore all feasible outcomes. This approach is limited in that the simulated virtual patient does not reflect the target population for treatment and the likely efficacy or incidence of adverse events. For this, population weighting needs to be applied. Introducing population-weighting simulations can reflect the mechanistic variability within the biology of a population, and thus provide a better reflection of the response in the target population.
The natural journey of investment for physiological modeling starts with the smaller targeted models to answer specific questions, and the generation of virtual cohorts that explore the hypothesis space around these questions. Typically these models and cohorts are useful for discovery and preclinical investigations where scientists can:
Having invested in smaller, more targeted models with a limited set of virtual patients, the next step of the investment journey is to expand the model with greater depth and breadth of pathophysiology, as well as to generate virtual populations, which are essential for precision medicine (Figure 3).
These added investments provide valuable contributions for later stage development where an understanding of the trial population response ahead of time is critical to defining the right trial and patient population:
Physiological modeling is opening up new opportunities to decipher the complexity, variability, and uncertainty within the pathophysiology of disease to provide improved insight, understanding, and decision-making for risk management and success. The elaborate and increasing complexity of remaining disease targets, the business transition from “me too” drug development, and the emergence of precision medicine all call for advanced modeling solutions.
As a result of the applicability, investment, and value of physiological modeling progressively increasing through the R&D drug development process, there is a natural journey of investment that scales with the therapeutic lifecycle. The correlation of the size of models along with the creation of virtual populations to support critical decisions in the R&D continuum means that companies can incrementally invest to support target to approval decisions.
With pharmaceutical company therapeutic focus spanning over decades, physiological simulation investments have the opportunity to be used, advanced, and reused time and time again as therapeutic groups investigate new targets, new therapies, and in-licensing opportunities.
The continued relevance and applicability of the models warrant a strategic investment in the drug industry toward creating complete disease and population models of the pathophysiology for each therapeutic area.
Tom Paterson is CTO of Entelos.
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