📒 Gadkar 2016
Quantitative systems pharmacology: a promising approach for translational pharmacology1
- drug development in the ‘post-genomic era’ => data science
- majority of Phase II and Phase III clinical trial failures are due to lack of efficacy
- more confidence in the mechanism of action is crucial for drug development in the early phase
- most novel drug mechanisms, however, human evidence is minimal if not absent during preclinical development
- In vitro: target & biomarkers
- In vivo (animal models): interspecies differences in physiology and pathology
- In silico (math modeling): pharmacokinetic–pharmacodynamic (PKPD), but not complex, highly regulated biology and disease in animals or humans typically.
- Quantitative Systems Pharmacology (QSP):
- Quantitative analysis of the dynamic interactions between drug(s) and a biological system that aims to understand the behavior of the system as a whole, as opposed to the behavior of its individual constituents
- Data-driven models to mechanistically driven ones
- Numerous potential applications of QSP exist in the preclinical and translational space
Evaluation of a novel target for treatment of asthma
- Agents targeting IgE (omalizumab) and IL-5 (mepolizumab) have been approved, and other agents targeting the IL-5, IL-4, and IL-13 cytokine pathways are in advanced development
- complex redundancy, feedback, and regulation of the numerous cells, pathways, and functions
- mechanistic information collated from expert knowledge and in vitro and preclinical data.
- Blinded predictions for response of severe asthmatic virtual patients to the anti-IL4R a antagonist (dupilumab) matched clinical results well => useful
- bridges complex mechanistic interactions and clinical knowledge to enable predictive simulations and exploration
Mechanistic support and combination strategy for ERK inhibition in BRAF mutant cancer
- mitogen-activated protein kinase (MAPK) signaling cascade, consisting of the RAS/RAF/MEK/ERK constitutively activated in many human cancers
- Inhibitors targeting BRAF (vemurafenib, dabrafenib) and MEK (cobimetinib, trametinib) are approved
- But there are poor response or acquired resistence
- acquired resistance to BRAF or MEK inhibitors show pathway rebound and remain responsive to ERK inhibition
- weighted virtual populations were then used to predict response rates to the ERK-inhibitor regimens, for which clinical data did not yet exist.
- predicted a synergistic response to the combination of MEK + ERK inhibition
PBPK guidance for intravenously administration of oseltamivir (Tamiflu) in pediatric patients (children)
- allow simulations of the concentration versus time profiles in plasma and tissues after dosing.
- superior predictive power achieved through translation of in vitro data into expected in vivo performance, well-suited to translational predictions
- streamline late stage drug development with avoidance of routine clinical studies
- Drug distributions and metabolism as well as age dependency
- In vivo, verified the reliability and plausibility of the modeling of oral and intravenous dosing in newborn monkeys => verification of simulations of oral dosing in infants and neonates
Values of QSP:
- identifying novel targets
- guiding preclinical study design
- predicting clinical PK based on physiological and molecule considerations
- predicting the potential for human efficacy and safety of novel targets and compounds
- evaluating or identifying potential biomarkers
- providing mechanistic understanding of efficacy, safety, and biomarkers
- evaluating combination therapy strategies for new molecules.
Gadkar K, Kirouac D, Parrott N, Ramanujan S. Quantitative systems pharmacology: aromising approach for translational pharmacology. Drug Discov. Today Technol. 2016;21-22:57-65. doi:10.1016/j.ddtec.2016.11.001. ↩︎