Periodic Reporting for period 1 - G4-mtQSAR (Identifying new anti-cancer drugs by computational multi-target approaches targeting the Gquadruplex DNA)
Période du rapport: 2021-06-01 au 2023-05-31
G-Quadruplexes (G4s) are guanine-rich four-stranded nucleic acid structures, which are abundantly found in the promoter region of various oncogenes (cMYC, cKIT, KRAS, etc.) and in the telomeric region. Overexpression of these oncogenes and telomerase-induced sustained elongation of telomeric DNA leads to deregulated cell division and cell immortalities. Such a phenomenon has been observed in many cancer pathologies. Thus Ligand-induced stabilization of G4s has been demonstrated to be efficient in targeted cancer therapy[1,2]. Simultaneous deregulation of multiple oncogenes is a major hurdle in treating a complex disease like cancer; simultaneously targeting multiple G4s associated with multiple oncogenes is beneficial.
The goal of the G4-mtQSAR project is to perform computational studies in a systematic way with the help of various machine learning and pharmacoinformatic methods to identify potential small lead molecules against G4 structures from various gene areas. Thus, the study aims at ‘stabilization of G4s with multi-target directed ligands (MTDL)’. Also, another major goal is to avail the automatic screening of potential G4 modulators by means of a completely novel drug discovery technological platform at MolDrug AI Systems SL company. Thus, the study delivers the first computational tool ‘G4-QuadScreen’ derived from a robust computational methodology with the functionality to screen out a library of small ligand molecules against G4 DNAs that are associated with cancer pathology.
Based on the previous background, the objectives of G4-mtQSAR were the following:
1. To identify potential MTDLs acting on various G4s associated with multiple oncogenes and thus assist in finding effective targeted anticancer therapeutic agents.
2. To accelerate the search for new leads against G4s and to reduce the false positive outcomes in the crucial early stages of drug discovery and development by promoting the use of computational advanced tools, models, and data analysis for G4-activity prediction as an alternative to traditional assays.
The goal of the G4-mtQSAR project is to perform computational studies in a systematic way with the help of various machine learning and pharmacoinformatic methods to identify potential small lead molecules against G4 structures from various gene areas. Thus, the study aims at ‘stabilization of G4s with multi-target directed ligands (MTDL)’. Also, another major goal is to avail the automatic screening of potential G4 modulators by means of a completely novel drug discovery technological platform at MolDrug AI Systems SL company. Thus, the study delivers the first computational tool ‘G4-QuadScreen’ derived from a robust computational methodology with the functionality to screen out a library of small ligand molecules against G4 DNAs that are associated with cancer pathology.
Based on the previous background, the objectives of G4-mtQSAR were the following:
1. To identify potential MTDLs acting on various G4s associated with multiple oncogenes and thus assist in finding effective targeted anticancer therapeutic agents.
2. To accelerate the search for new leads against G4s and to reduce the false positive outcomes in the crucial early stages of drug discovery and development by promoting the use of computational advanced tools, models, and data analysis for G4-activity prediction as an alternative to traditional assays.
A state-of-the-art software tool has been developed where all the successful multi-target QSAR models and in-house ADMET models were incorporated as a knowledge-base. The software has then been used to screen potential MTDLs against multiple G4s. The selectivity and binding characteristics of the screened MTDLs towards G4s over duplex DNA were consequently analysed by performing various in-silico and in-vitro assays.
The project tasks were:
⦁ Chemical and biological data curation of the collected experimental data. A comprehensive literature survey has been performed for identifying various ligand molecules along with their activity against various G4 motifs. Extensive data curation has been performed, including a complete checking & rectifying of errors in the chemical structure, exclusive handling of inorganic/organometallic/salts, normalization of the chemical structures, duplicate analysis, activity-cliff analysis etc
⦁ Multi-target QSAR models against different types of G4s have been developed. The type of models (regression and classification-based QSAR models) have been developed depending on the type of collected response data (continuous and categorical, respectively). The chemical numerical descriptors have been computed using available in-house python script and other freely available software. The list of descriptors included several classes such as constitutional, atom centered, connectivity indices, edge adjacency, electro-topological state, walk path counts, functional group, etc. As applicable, several linear and non-linear chemometric techniques have been employed to develop the models. Finally, the QSAR models have been evaluated using the standard protocol recommended by the OECD Guidelines.
⦁ An AI user-friendly, platform-independent software tool which utilizes the knowledge gained from the modeling study as well as the developed QSAR models to screen, optimize and/or design MTDLs against G4. This AI platform is based on KNIME nodes and KNIME workflow schemes.
⦁ A virtual screening campaign has been done using desirability-based multi-objective optimization, in silico and experimental evaluation of screened MTDLs. We have performed the virtual screening of big chemical space (databases such as, ZINC, Maybridge, DrugBank, InterBioScreen natural and Super Natural II, etc.), while employing desirability-based MOO approach. Screened ligands have been evaluated using molecular docking, MD simulations and key biophysical assays.
⦁ Events (conference, workshop, and other events) attended or conducted by the researcher: nine events described in the Tech. Report (Part B)
⦁ The software tool ‘G4-QuadScreen’ will be copyrighted. Our original intention was to also protect the models developed in the project, however, the hired agency to handle IPR informed us that there is no way to protect the models. However, since the models are implemented and automated in G4-QuadScreen, the original and validated models developed in this project will be available for end-users interested on them.
The project tasks were:
⦁ Chemical and biological data curation of the collected experimental data. A comprehensive literature survey has been performed for identifying various ligand molecules along with their activity against various G4 motifs. Extensive data curation has been performed, including a complete checking & rectifying of errors in the chemical structure, exclusive handling of inorganic/organometallic/salts, normalization of the chemical structures, duplicate analysis, activity-cliff analysis etc
⦁ Multi-target QSAR models against different types of G4s have been developed. The type of models (regression and classification-based QSAR models) have been developed depending on the type of collected response data (continuous and categorical, respectively). The chemical numerical descriptors have been computed using available in-house python script and other freely available software. The list of descriptors included several classes such as constitutional, atom centered, connectivity indices, edge adjacency, electro-topological state, walk path counts, functional group, etc. As applicable, several linear and non-linear chemometric techniques have been employed to develop the models. Finally, the QSAR models have been evaluated using the standard protocol recommended by the OECD Guidelines.
⦁ An AI user-friendly, platform-independent software tool which utilizes the knowledge gained from the modeling study as well as the developed QSAR models to screen, optimize and/or design MTDLs against G4. This AI platform is based on KNIME nodes and KNIME workflow schemes.
⦁ A virtual screening campaign has been done using desirability-based multi-objective optimization, in silico and experimental evaluation of screened MTDLs. We have performed the virtual screening of big chemical space (databases such as, ZINC, Maybridge, DrugBank, InterBioScreen natural and Super Natural II, etc.), while employing desirability-based MOO approach. Screened ligands have been evaluated using molecular docking, MD simulations and key biophysical assays.
⦁ Events (conference, workshop, and other events) attended or conducted by the researcher: nine events described in the Tech. Report (Part B)
⦁ The software tool ‘G4-QuadScreen’ will be copyrighted. Our original intention was to also protect the models developed in the project, however, the hired agency to handle IPR informed us that there is no way to protect the models. However, since the models are implemented and automated in G4-QuadScreen, the original and validated models developed in this project will be available for end-users interested on them.
The research project included in silico and in vitro approaches associated with pharmaceutical studies that in combination with Dr. Ambure’s pharmaceutical background indeed helped her to gain interdisciplinary proficiency in her scientific field. This project upgraded her skills in the field of drug discovery, at the same time the exposure to various functions of research projects helped her in acquiring the managerial skills needed to lead a team. She learned the entire process of QSAR modeling, developing web-based software tool to deploy the models for screening purposes, virtual screening, and biophysical evaluation of screened compounds. Thus, she conducted all the steps of drug discovery necessary before animal modeling. It is a great learning experience for a researcher in the pharmaceutical field. During the project, she attended several online international events and two in-presence international conferences that allowed her to connect with the international scientific community working in the field of QSAR, machine learning, oncology, and various areas of drug discovery. She has earned a capacity to independently handle research projects of diverse nature within the scope of molecular modeling and drug discovery field. The project has already resulted in a publication in a peer-reviewed international journal.