Services often involve, but are not limited to:
- Early feasibility assessment
- Knowledge gap analysis
- Experiment prioritization and design
- Best-in-class drug property identification
- Technical due diligence for in-licensing opportunities
- Portfolio prioritization
- Disease or pathway model development
- Combination therapy
- Efficacious dose projection
- Biomarker identification
- PKPD translation from animal to human
Applied BioMath’s focus in the development phase is to protect first mover advantage and help our partners develop best-in-class drugs. Because our models are mechanistic and incorporate all relevant data (in vitro, in vivo, preclinical, and clinical), we quickly determine what parameters, such as affinity, dose, and half-life are required to be competitive and best-in-class. We simulate best and worst case scenarios to determine what kind of experiments should be performed to have a better understanding of the drug candidate and reduce uncertainty in human dose predictions. This accelerates lead generation, candidate selection and best prepares for GLP toxicology studies, saving significant time and money.
Applied BioMath offers support for GLP-toxicology studies through Phase 2, including pre-IND and IND reports as well as pharmacoeconomics. We help design clinical trials by determining patient selection, sample collection times, biomarker selection, and how high and what frequency of a dose is required to assess proof of clinical concept. Our models also help you accurately predict first-in-human doses.