Transparent Machine Learning Model to Understand Drug Permeability through the Blood–Brain Barrier DOI Creative Commons

Hengjian Jia,

Gabriele C. Sosso

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

The blood–brain barrier (BBB) selectively regulates the passage of chemical compounds into and out central nervous system (CNS). As such, understanding permeability drug molecules through BBB is key to treating neurological diseases evaluating response CNS medical treatments. Within last two decades, a diverse portfolio machine learning (ML) models have been regularly utilized as tool predict, and, much lesser extent, understand, several functional properties medicinal drugs, including their propensity pass BBB. However, most numerically accurate date lack in transparency, they typically rely on complex blends different descriptors (or features or fingerprints), many which are not necessarily interpretable straightforward fashion. In fact, "black-box" nature these has prevented us from pinpointing any specific design rule craft next generation pharmaceuticals that need not) this work, we developed ML model leverages an uncomplicated, transparent set predict addition its simplicity, our achieves comparable results terms accuracy compared state-of-the-art models. Moreover, use naive Bayes analytical provide further insights structure–function relation underpins capacity given molecule Although computational rather than experimental, identified molecular fragments groups may significantly impact drug's likelihood permeating This work provides unique angle problem lays foundations for future aimed at leveraging additional descriptors, potentially obtained via bespoke dynamics simulations.

Language: Английский

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges DOI Creative Commons
Li Lin, Ramón A. Álvarez‐Puebla, Luis M. Liz‐Marzán

et al.

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 24, 2025

The year 2024 marks the 50th anniversary of discovery surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity molecular specificity. This review summarizes advancements challenges substrates, nanotags, instrumentation, spectral analysis for biomedical applications. We highlight key developments colloidal solid an emphasis on surface chemistry, hotspot design, 3D hydrogel plasmonic architectures. Additionally, we introduce innovations including those interior gaps, orthogonal reporters, near-infrared-II-responsive properties, along biomimetic coatings. Emerging technologies such as optical tweezers, nanopores, wearable sensors have expanded capabilities single-cell single-molecule analysis. Advances analysis, signal digitalization, denoising, deep learning algorithms, improved quantification complex biological data. Finally, this discusses applications nucleic acid detection, protein characterization, metabolite monitoring, vivo spectroscopy, emphasizing potential liquid biopsy, metabolic phenotyping, extracellular vesicle diagnostics. concludes perspective clinical translation SERS, addressing commercialization potentials tissue sensing imaging.

Language: Английский

Citations

3
Toxic Alerts of Endocrine Disruption Revealed by Explainable Artificial Intelligence DOI Creative Commons

Lucca Caiaffa Santos Rosa,

Mariam Sarhan,

André Silva Pimentel

et al.

Environment & Health, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

The local interpretable model-agnostic explanation method was used to unveil substructures (toxic alerts) that cause endocrine disruption in chemical compounds using machine learning models. random forest classifier applied build explainable models with the TOX21 data sets after curation. Using these EDC and EDKB-FDA sets, were unveiled, providing stable, more specific, consistent explanations, which are essential for trust acceptance of findings, mainly due difficulty finding relevant experimental evidence different receptors (androgen, estrogen, aryl hydrocarbon, aromatase, peroxisome proliferator-activated receptors). This approach is significant because its contribution interpretability algorithms, particularly context unveiling associated five targets (androgen receptor, estrogen hydrocarbon receptors, aromatase receptors), thereby advancing field environmental toxicology, where a careful evaluation potential risks exposure new needed. specific thiophosphate, sulfamate, anilide, carbamate, sulfamide, thiocyanate presented as toxic alerts better understand their adverse effects on human health environment.

Language: Английский

Citations

0
Prediction of the Extent of Blood–Brain Barrier Transport Using Machine Learning and Integration into the LeiCNS-PK3.0 Model DOI Creative Commons
Berfin Gülave, Helle W. van den Maagdenberg,

Luke van Boven

et al.

Pharmaceutical Research, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

0
High throughput screening identifies potential inhibitors targeting trimethoprim resistant DfrA1 protein in Klebsiella pneumoniae and Escherichia coli DOI Creative Commons
Soharth Hasnat,

Soaibur Rahman,

Mohammad Sayed Alam

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 28, 2025

The DfrA1 protein provides trimethoprim resistance in bacteria, especially Klebsiella pneumoniae and Escherichia coli, by modifying dihydrofolate reductase, which reduces the binding efficacy of antibiotic. This study identified inhibitors trimethoprim-resistant through high-throughput computational screening optimization 3,601 newly synthesized chemical compounds from ChemDiv database, aiming to discover potential drug candidates targeting K. E. coli. Through this approach, we six promising DCs, labeled DC1 DC6, as DfrA1. Each DC showed a strong ability bind effectively formed favorable interactions at sites. These were comparable those Iclaprim, well-known antibiotic effective against To confirm our findings, explored how DCs work molecular level, focusing on their thermodynamic properties. Additionally, dynamics simulations confirmed these inhibit protein. Our results that DC4 (an organofluorinated compound) DC6 (a benzimidazole exhibited than control drug, particularly regarding stability, solvent-accessible surface area, solvent exposure, polarity, site interactions, influence residence time efficacy. Overall, findings suggest have act DfrA1, offering prospects for treatment management infections caused coli both humans animals. However, further vitro validations are necessary.

Language: Английский

Citations

0
Machine Learning in Drug Development for Neurological Diseases: A Review of Blood Brain Barrier Permeability Prediction Models DOI Creative Commons
A.H.M. Nurun Nabi,

Pedram Pouladvand,

Litian Liu

et al.

Molecular Informatics, Journal Year: 2025, Volume and Issue: 44(3)

Published: March 1, 2025

The blood brain barrier (BBB) is an endothelial-derived structure which restricts the movement of certain molecules between general somatic circulatory system to central nervous (CNS). While BBB maintains homeostasis by regulating molecular environment induced cerebrovascular perfusion, it also presents significant challenges in developing therapeutics intended act on CNS targets. Many drug development practices rely partly extensive cell and animal models predict, extent, whether prospective therapeutic can cross BBB. In interest reduce costs improve prediction accuracy, many propose using advanced computational modeling permeability profiles leveraging empirical data. Given scale growth machine learning deep learning, we review most recent approaches predicting permeability.

Language: Английский

Citations

0
Synthetic Approaches, Properties, and Applications of Acylals in Preparative and Medicinal Chemistry DOI Creative Commons

Tobias Keydel,

Andreas Link

Molecules, Journal Year: 2024, Volume and Issue: 29(18), P. 4451 - 4451

Published: Sept. 19, 2024

Diesters of geminal diols (R-CH(O-CO-R′)2, RR′C(OCOR″)2, etc. with R = H, aryl or alkyl) are termed acylals according to IUPAC recommendations (Rule P-65.6.3.6 Acylals) if the acids involved carboxylic acids. Similar condensation products can be obtained from various other acidic structures as well, but these related “non-classical acylals”, one might call them, differ in aspects classical and will not discussed this article. Carboxylic acid diesters play a prominent role organic chemistry, only their application protective groups for aldehydes ketones also precursors total synthesis natural compounds variety reactions. What is more, useful key structural motif clinically validated prodrug approaches. In review, we summarise syntheses chemical properties such show what potentially under-explored possibilities exist field drug design, especially prodrugs, classify functional group medicinal chemistry.

Language: Английский

Citations

1
High-Throughput Screening Reveals Potential Inhibitors Targeting Trimethoprim-Resistant DfrA1 Protein in Klebsiella pneumoniae and Escherichia coli DOI Creative Commons
Soharth Hasnat,

Soaibur Rahman,

Mohammad Sayed Alam

et al.

Published: Nov. 18, 2024

Abstract The DfrA1 protein provides trimethoprim resistance in bacteria, especially Klebsiella pneumoniae and Escherichia coli , by modifying dihydrofolate reductase, which reduces the binding efficacy of antibiotic. Thus, this study aimed to identify inhibitors trimethoprim-resistant through high-throughput computational screening 3,601 newly synthesized chemical compounds sourced from ChemDiv database. We conducted optimization a library containing against K. E. potential drug candidates (DCs). Through extensive approach, we identified six promising DCs, labeled DC1 DC6, as DfrA1. Each DC demonstrated strong initial affinity favorable interactions with sites when compared effective Iclaprim (effective antibiotic DfrA1), used control. To validate these findings, further investigated molecular mechanisms inhibition, focusing on thermodynamic properties DCs. Furthermore, dynamics simulation (MDS) validated inhibitory DCs protein. Our results showed that DC4 (an organoflourinated compound) DC6 (a benzimidazol superior than control drug, particularly regarding stability, solvent-accessible surface area, solvent exposure, polarity, site interactions, influence their residence time efficacy. Overall, findings suggest have act DfrA1, offering prospects for treatment management infections caused both humans animals.

Language: Английский

Citations

0
Transparent Machine Learning Model to Understand Drug Permeability through the Blood–Brain Barrier DOI Creative Commons

Hengjian Jia,

Gabriele C. Sosso

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 18, 2024

The blood–brain barrier (BBB) selectively regulates the passage of chemical compounds into and out central nervous system (CNS). As such, understanding permeability drug molecules through BBB is key to treating neurological diseases evaluating response CNS medical treatments. Within last two decades, a diverse portfolio machine learning (ML) models have been regularly utilized as tool predict, and, much lesser extent, understand, several functional properties medicinal drugs, including their propensity pass BBB. However, most numerically accurate date lack in transparency, they typically rely on complex blends different descriptors (or features or fingerprints), many which are not necessarily interpretable straightforward fashion. In fact, "black-box" nature these has prevented us from pinpointing any specific design rule craft next generation pharmaceuticals that need not) this work, we developed ML model leverages an uncomplicated, transparent set predict addition its simplicity, our achieves comparable results terms accuracy compared state-of-the-art models. Moreover, use naive Bayes analytical provide further insights structure–function relation underpins capacity given molecule Although computational rather than experimental, identified molecular fragments groups may significantly impact drug's likelihood permeating This work provides unique angle problem lays foundations for future aimed at leveraging additional descriptors, potentially obtained via bespoke dynamics simulations.

Language: Английский

Citations

0