Providing quantitative guidance for drug R&D is complicated. It requires excellent science, sophisticated rules of engagement for collaborating, and the right mix of people. Applied BioMath's vast experience providing these services over the years helped us identify three critical foundations that are vital to the success of each project we do: our expertise in mechanistic modeling, an iterative approach to collaborating, and a diverse team. Each of these foundations plays a major role in our ability to deliver the best science to you in an efficient manner.
Mechanistic Modeling leverages knowledge of physiology, biochemistry, cell biology, biophysics, and drug properties (e.g., drug affinity, relevant cell types, ligand-receptor kinetics, disease biology, etc.) to create a biologically relevant mathematical model. A mechanistic model incorporates all relevant preclinical and clinical data.
Mechanistic models can be leveraged throughout all of drug R&D, from early discovery through clinical research. These models help identify knowledge gaps and inform experiment design, help improve the understanding of translational value of animal models, and help select the right animal model for your question. Mechanistic models typically provide better human predictions for large molecules and novel therapies and allow mechanistic translation to different indications and patient populations for all therapeutics.
The Applied BioMath collaboration process is iterative and highly collaborative. We typically meet with our collaborators every three weeks with additional ad-hoc meetings as necessary. Initial discussion topics typically focus on:
- What biological mechanisms inform the model diagram, that is, the network or model schema.
- The relevant data for the model and metrics (e.g., surrogates of efficacy), where needed, to benchmark and qualify the model.
- Which parts of the model are mechanistic and which parts are semi-mechanistic or statistical.
The Applied BioMath team includes a variety of backgrounds spanning drug development, biology, mathematics, and computer science. Our varied backgrounds are beneficial because we often collaborate with teams just as varied. We are able to understand and relate to all client team members, and incorporating everyone’s insights often accelerates model development and results. Our team is a blend of biologists, kineticists, biochemists, mathematicians, pharmacologists, engineers, and physicists. Our biology experience is expansive spanning specialties such as biophysics, cancer biology, immunology, neuroscience, and cell biology to name a few. Every project team is comprised of at least one biologist and one modeler.