Biology subjects

Baylon, J. L.

Publications and source records attributed to Baylon, J. L..

5 recordsLinked to original sources

bioRxiv2025

Balanced Permeability Index is a Strong Predictor of Intestinal Absorption and Oral Bioavailability for Heterobifunctional Ligand-Directed Degraders

Ligand-directed degraders (LDDs) are heterobifunctional molecules that degrade proteins by engaging the ubiquitin-protein ligase (E3) system. LDDs consist of a target-engaging moiety, an E3 ligase-binding moiety and a bridging linker. Due to their size and physicochemical complexity these molecules do not adhere to well-established rules of lead optimization. The optimization of passive permeability remains a key challenge to develop orally bioavailable LDDs. To overcome this challenge, in this study we demonstrate that the Balanced Permeability Index (BPI)--a new metric that combines size, polarity and lipophilicity--is highly predictive of oral bioavailability for LDDs. Here, we introduce an additional parameter--called smallest maximum intramo-lecular distance (SMID)--to the original BPI index to account for cross sectional area of LDDs, termed BPILDD. With this new parameter, BPILDD can differentiate oral bioavailability of LDDs in our dataset more effectively than polarity, lipophilicity, or size separately. In addition, BPILDD is also more effective at identifying orally bioavailable LDDs than some in vitro measurements of cell permeability that traditionally inform bioavailability. This finding opens the possibility of employing BPILDD for the design and optimization of orally bioavailable LDDs to improve their drug metabolism and pharmacokinetics properties.

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↗

bioRxiv2021

PepSeA: Peptide Sequence Alignment and Visualization Tools to Enable Lead Optimization

Therapeutic peptides offer potential advantages over small molecules in terms of selectivity, affinity, and their ability to target "undruggable" proteins that are associated with a wide range of pathologies. Despite their importance, there are currently no adequate molecular design capabilities that inform medicinal chemistry decisions on peptide programs. More specifically, SAR (Structure-Activity Relationship) analysis and visualization of linear, cyclic, and cross-linked peptides containing non-natural motifs, which are widely used in drug discovery. To bridge this gap, we developed PepSeA (Peptide Sequence Alignment and Visualization), an open-source, freely available package of sequence-based tools (https://github.com/Merck/PepSeA). PepSeA enables multi-sequence alignment of non-natural amino acids and enhanced HELM (Hierarchical Editing Language for Macromolecules) visualization. Via stepwise SAR analysis of a ChEMBL peptide dataset, we demonstrate PepSeAs power to accelerate decision making in lead optimization campaigns in pharmaceutical settings. PepSeA represents an initial attempt to expand cheminformatics capabilities for therapeutic peptides and to enable rapid and more efficient design-make-test cycles.

bioinformatics↗

bioRxiv2020

Anti-inflammatory dopamine- and serotonin-based endocannabinoid epoxides reciprocally regulate cannabinoid receptors and the TRPV1 channel

The endocannabinoid (eCB) system is a promising target to mitigate pain as the eCBs are endogenous ligands of the pain-mediating receptors--cannabinoid receptors 1 and 2 (CB1 and CB2) and TRPV1. Here we report on a novel class of lipids formed by the epoxidation of N-arachidonoyl-dopamine (NADA) and N-arachidonoyl serotonin (NA5HT) by cytochrome P450 (CYP) epoxygenases. These epoxides (epoNADA and epoNA5HT) are dual-functional rheostat modulators (varying strength of agonism or antagonism) of the eCB-TRPV1 axis. In fact, epoNADA is a 6-fold stronger agonist of TRPV1 than NADA while epoNA5HT is a 30-fold stronger antagonist of TRPV1 than NA5HT and displays a significantly stronger inhibition on TRPV1-mediated responses in primary afferent neurons. Moreover, epoNA5HT is a full CB1 agonist. The epoxides reduce the pro-inflammatory biomarkers IL-6, IL-1{beta}, TNF- and nitrous oxide (NO) and raise anti-inflammatory IL-10 in activated microglial cells. The epoxides are spontaneously generated by activated microglia cells and their formation is potentiated in the presence of another eCB, anandamide (AEA). We provide evidence for the direct biochemical mechanism of this potentiation using human CYP2J2, a CYP epoxygenase in the human brain, using detailed kinetics studies and molecular dynamics simulations. Taken all together, inflammation leads to an increase in the metabolism of NADA, NA5HT and other eCBs by CYP epoxygenases to form the corresponding epoxides. The epoxide metabolites are bioactive lipids that are more potent, multi-faceted endogenous molecules, capable of influencing the activity of CB1, CB2 and TRPV1 receptors. The identification of these molecules will serve as templates for the synthesis of new multi-target therapeutics for the treatment of inflammatory pain.

biochemistry↗