【For INTERNATIONAL COLLABORATIVE RESEARCH FY2023】

SUMMARY of

DATE: Day 30 Month April Year 2024

FY2023 RESEARCH RESULTS REPORTFor International Collaborative Research with IPR, Osaka University
Research Title

Artificial Intelligence-guided discovery of novel quorum sensing inhibitors
as next-generation therapeutics against Pseudomonas aeruginosa infection

ApplicantName

Rajnish Kumar

Affiliation

Department of Pharmaceutical Engineering & Technology
Indian Institute of Technology (B.H.U.)
Varanasi-221005 (U.P.), India

Present Title

Assistant Professor

Research Collaborator (Host PI)

Kenji Mizuguchi

Summary
Pseudomonas aeruginosa (PA) is a major nosocomial threat, causing severe infections in vulnerable patient groups.
Its growing resistance to multiple drugs prompted its classification as a critical pathogen by the World HealthOrganization, necessitating new treatment approaches. PA's pathogenesis relies on motility, virulence factor
production, and biofilm formation, which impede antibiotic effectiveness. These factors are controlled by quorum
sensing (QS) systems, such as LasR/I, RhlR/I, and PQS. Targeting these pathways shows promise in reducing
virulence and enhancing antibiotic susceptibility. However, despite efforts to inhibit LasR/RhlR, no compounds
have advanced to clinical use. I have been collaborating with Prof. Mizuguchi since June 2019 and have been
exploring opportunities for joint research work. In this IPR-International Collaborative Research Program 2023, we
discussed the use of DruMap, an ML-based pharmacokinetic prediction server developed by his lab for predicting
pharmacokinetic parameters for candidate compounds, and his lab’s expertise in building machine learning models
for the ultra-large virtual screening of LasR inhibitors.
We have used Deep Docking, an AI-based tool to virtually screen ultra-large libraries library of 1.3 billion
compounds from the ZINC20 database to identify hit compounds against the LasR. 0.1% molecules were samples
from the whole dataset and then subjected to docking into the binding site of LasR and the docking score wasobtained which was used to train CNN-based models using TensorFlow. The final model effectively screened 116
million molecules, demonstrating good generalization capabilities with a recall of 0.9029 and precision of 0.068
with a model cutoff of -10.10 kcal/mol. The last phase of model building and screening is still ongoing to reduce
the number of finally selected hits which will be procured and validated in vitro using a battery of assays to assess
QS inhibition and anti-virulence activity, alongside cytotoxicity testing to ensure compound safety.
Physicochemical and pharmacokinetic profiling helped select optimal hit compounds, considering parameters suchas solubility, chemical stability, and ADMET properties. The project's anticipated outcomes include novel QSIs
with potential as anti-virulence therapeutics against multi-resistant PA infections. Once, we identify and validate
the hit compounds, we expect to collaborate with Prof. Mizuguchi to optimize those hit compounds using
molecular dynamics simulation and ML-based lead optimization. The expected results of this project are novel QS
inhibitors and their development into potential anti-virulence therapeutics for the treatment of multi-resistant PAinfections.
*Deadline: May 10, 2024
*Please submit it to E-mail: tanpakuken-kyoten@office.osaka-u.ac.jp.
*Please describe this summary within 1 sheet. Please DON’T add some sheets.
*This summary will be published on the web.