Services often involve, but are not limited to:
- PKPD modeling for clinical data
- Population PK
- GLP toxicity study design
- Efficacious dose projection
- Species translation
- Therapeutic indication and patient population selection
- Pre-IND and IND support
- Optimal clinical trial design and analysis
- Disease model development
- Combination therapy
- Knowledge gap analysis
- Experiment prioritization and design
- Technical due diligence for in-licensing opportunities
Applied BioMath’s early discovery services help drug development teams assess the likelihood that a drug candidate can be developed for a drug target. Questions such as whether the candidate will meet the desired route of administration and dosing schedules, whether the target should be the ligand or the receptor, as well as critical Go/No-go decisions around whether to pursue the proposed target are assessed. This helps identify failures early and prioritize what experiments are necessary moving forward, ultimately reducing project timelines and cost.
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.