Biology subjects

Broccatelli, F.

Publications and source records attributed to Broccatelli, F..

3 recordsLinked to original sources

bioRxiv2024

Estimating Organ-to-Plasma Ratios in physiologically based PK modeling: a simplified approach for early drug discovery

Physiologically based pharmacokinetics (PBPK) modeling is a valuable tool in drug development and candidate selection. However, its widespread adoption in early stages of drug discovery remains limited. This study proposes a novel simplified approach to estimate organ-to-plasma ratio (Kp) values based on measured or estimated volume of distribution (VDss) to eliminate the reliance on additional in vitro data such as blood to plasma partition, LogP and pKa, which may not be available for newly synthesized molecules. The study includes 11 simulations across 4 compounds and 5 species. When simulations were based on ideal VDss and clearance (CL) values determined through non-compartmental analysis (NCA), the observed average absolute fold error (AAFE) aligned with experimental variability in the in vivo experiments (AAFE=1.3). However, when in vitro to in vivo correlation approaches were used to predict VDss and CL starting from in vitro intrinsic CL, plasma protein binding and microsomal binding, the AAFE increased to 1.7, reflecting the additional error introduced by the use of in vitro data to determine PK properties. Overall, this work provides a starting point for the facile implementation of PBPK models to meet the needs of early drug discovery projects.

pharmacology and toxicology↗

bioRxiv2024

Application of mechanistic multiparameter optimization and large scale in vitro to in vivo pharmacokinetics correlations to small molecule therapeutic projects

Computational chemistry and machine learning are used in drug discovery to predict target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g. minimizing dose, maximizing safety margins and/or minimized drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo PK properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top 2nd percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared to the rest of the compounds synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

pharmacology and toxicology↗

bioRxiv2023

Balanced Permeability Index: a multi-parameter index for improved in-vitro permeability

The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. The BPI can also be used to guide optimization in non-traditional small molecule modalities, such as protein degraders, which often lie outside of traditional small molecule space. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

bioinformatics↗