Tanya Paquet

University of Cape Town

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Leslie J. Street

University of Cape Town

Claire Le Manach

University of Cape Town

Gintautas Kamuntavičius

Vytautas Magnus University

Sergio Wittlin

University of Basel

Roy Tal

Menlo School

Povilas Norvaišas

Institute of Structural and Molecular Biology

Dale Taylor

University of Cape Town

Diego Gonzàlez Cabrera

University of Cape Town

Orestis Bastas

King's College Hospital

Alvaro Prat

Imperial College London

Cooperative Institutions

University of Cape Town

Inserm

South African Medical Research Council

Université Paris Cité

University College London

Medicines for Malaria Venture

University of Pretoria

Assistance Publique – Hôpitaux de Paris

National Institutes of Health

Columbia University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

Identification of Fast-Acting 2,6-Disubstituted Imidazopyridines That Are Efficacious in the in Vivo Humanized Plasmodium falciparum NODscidIL2Rγ^null Mouse Model of Malaria

Journal of Medicinal Chemistry (2018)

Aloysius T. Nchinda Claire Le Manach Tanya Paquet Diego Gonzàlez Cabrera Kathryn J. Wicht

Optimization of a chemical series originating from whole-cell phenotypic screening against the human malaria parasite, Plasmodium falciparum, led to the identification of two promising 2,6-disubstituted imidazopyridine compounds, 43 and 74. These compounds exhibited potent activity against asexual blood stage parasites that, together with their in vitro absorption, distribution, metabolism, and excretion (ADME) properties, translated to in vivo efficacy with clearance of parasites in the PfSCID mouse model for malaria within 48 h of treatment.

ADME

10.1021/acs.jmedchem.8b00382

Cite

Citations (19)

SE(3) Equivariant Topologies for Structure-based Drug Discovery

Alvaro Prat Hisham Abdel Aty Aurimas Pabrinkis Orestis Bastas Tanya Paquet

Modeling protein-ligand interactions is a challenging task that has been approached through an array of perspectives. From physics-based computational approaches to vast deep learning pipelines, in silico methods hold promise in reducing experimental overhead in the otherwise tedious and costly drug discovery campaigns. We introduce Protein-Ligand Equivariant Transformer (ProLET), a generalizable model built upon chemically inspired SE(3) equivariant geometric deep learning. We evaluate ProLET on a wide range of established standards, including the notoriously difficult PoseBusters and Merck’s FEP benchmarks, consistently demonstrating superior performance in binding affinity prediction and pose estimation. We demonstrate its effectiveness across different stages in drug discovery, showing that ProLET can be used for lead optimization and hit identification as well as for prioritizing compounds that are selective towards a desired target. By bridging the gap between accuracy, efficiency, and generalizability, ProLET stands as a powerful and adaptive resource, signifying a step towards safe and reliable AI-driven drug discovery.

Equivariant map

10.26434/chemrxiv-2024-5q7ts

Cite

Citations (0)

Antimalarial Aminothiazoles and Aminopyridines From Phenotypic Whole-Cell Screening Of A SoftFocus ^® Library

Future Medicinal Chemistry (2012)

Tanya Paquet Richard K. Gordon David Waterson Michael J. Witty Kelly Chibale

The current state of antimalarial drug resistance emphasizes the need for new therapies with novel modes of action that will add a significant benefit compared with current standards. In this regard, high throughput phenotypic whole-cell screening aids the discovery of novel antiplasmodial scaffolds that are inherently suited to hit-to-lead and lead-optimization efforts. The aminothiazoles and aminopyridines exemplify two such compound classes stemming from whole-cell screening. Respective structure–activity relationship determinations and subsequent optimization around these scaffolds led to frontrunner compounds in each series, which possess the desired antimalarial efficacy, bioavailability and metabolic stability to further progress medicinal chemistry programs.

Aminopyridines

Phenotypic screening

High-Throughput Screening

10.4155/fmc.12.176

Cite

Citations (27)

Structure–Activity Relationship Studies of Orally Active Antimalarial 2,4-Diamino-thienopyrimidines

Journal of Medicinal Chemistry (2015)

Diego Gonzàlez Cabrera Frédèric Douelle Claire Le Manach Ze Han Tanya Paquet

Based on the initial optimization of orally active antimalarial 2,4-diamino-thienopyrimidines and with the help of metabolite identification studies, a second generation of derivatives involving changes at the 2- and 4-positions of the thienopyrimidine core were synthesized. Improvements in the physiochemical properties resulted in the identification of 15a, 17a, 32, and 40 as lead molecules with improved in vivo exposure. Furthermore, analogue 40 exhibited excellent in vivo antimalarial activity when dosed orally at 50 mg/kg once daily for 4 days in the Plasmodium berghei mouse model, which is superior to the activity seen with previously reported compounds, and with a slightly improved hERG profile.

Plasmodium berghei

hERG

Active metabolite

Structure–activity relationship

10.1021/acs.jmedchem.5b01156

Cite

Citations (16)

Accelerated Hit Identification with Target Evaluation, Deep Learning and Automated Labs: Prospective Validation in IRAK1

Gintautas Kamuntavičius Alvaro Prat Tanya Paquet Orestis Bastas Hisham Abdel Aty

In this study, we synergistically integrate Ro5's target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with Strateos automated robotic cloud lab optimized for ultra high-throughput screening, to experimentally validate Ro5's tools. This integrated approach leads to a significant acceleration in the processes of target identification and hit discovery. By using SpectraView to select IRAK1 as the focal point of our investigation, we prospectively validate HydraScreen structure-based deep learning model. We can achieve the identification of an 23.8% of all IRAK1 hits within the top 1% of ranked compounds. HydraScreen also outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, two of which represent novel candidates for IRAK1 and hold potential for future development. Our platforms and innovative tools promise to expedite the early stages of drug discovery.

Identification

Drug target

10.26434/chemrxiv-2023-mh22x-v4

Cite

Citations (0)

Structure–activity relationship studies of antiplasmodial aminomethylthiazoles

Bioorganic & Medicinal Chemistry Letters (2014)

Peter Mubanga Cheuka Diego Gonzàlez Cabrera Tanya Paquet Kelly Chibale

IC50

Structure–activity relationship

10.1016/j.bmcl.2014.09.071

Cite

Citations (3)

2,4-Diaminothienopyrimidines as Orally Active Antimalarial Agents

Journal of Medicinal Chemistry (2014)

Diego Gonzàlez Cabrera Claire Le Manach Frédèric Douelle Yassir Younis Tzu‐Shean Feng

A novel series of 2,4-diaminothienopyrimidines with potential as antimalarials was identified from whole-cell high-throughput screening of a SoftFocus ion channel library. Synthesis and structure-activity relationship studies identified compounds with potent antiplasmodial activity and low in vitro cytotoxicity. Several of these analogues exhibited in vivo activity in the Plasmodium berghei mouse model when administered orally. However, inhibition of the hERG potassium channel was identified as a liability for this series.

hERG

Plasmodium berghei

Structure–activity relationship

10.1021/jm401760c

Cite

Citations (35)

Contents

Journal of Hepatology (2013)

Maurizia Rossana Brunetto P Marcellin B. Cherubini Cihan Yurdaydın P Farci

10.1016/s0168-8278(13)00626-0

Cite

Citations (0)

Accelerated Hit Identification with Target Evaluation, Deep Learning and Automated Labs: Prospective Validation in IRAK1

Gintautas Kamuntavičius Alvaro Prat Tanya Paquet Orestis Bastas Hisham Abdel Aty

Background: Target identification and hit identification can be transformed through the application of biomedical knowledge analysis, AI-driven virtual screening and robotic cloud lab systems. However there are few prospective studies that evaluate the efficacy of such integrated approaches. Results: We synergistically integrate our in-house-developed target evaluation (SpectraView) and deep-learning-driven virtual screening (HydraScreen) tools with an automated robotic cloud lab designed explicitly for ultra-high-throughput screening, enabling us to validate these platforms experimentally. By employing our target evaluation tool to select IRAK1 as the focal point of our investigation, we prospectively validate our structure-based deep learning model. We can identify 23.8% of all IRAK1 hits within the top 1% of ranked compounds. The model outperforms traditional virtual screening techniques and offers advanced features such as ligand pose confidence scoring. Simultaneously, we identify three potent (nanomolar) scaffolds from our compound library, 2 of which represent novel candidates for IRAK1 and hold promise for future development. Conclusion: This study provides compelling evidence for SpectraView and HydraScreen to provide a significant acceleration in the processes of target identification and hit discovery. By leveraging Ro5's HydraScreen and Strateos' automated labs in hit identification for IRAK1, we show how AI-driven virtual screening with HydraScreen could offer high hit discovery rates and reduce experimental costs. Scientific contribution: We present an innovative platform that leverages Knowledge graph-based biomedical data analytics and AI-driven virtual screening integrated with robotic cloud labs. Through an unbiased, prospective evaluation we show the reliability and robustness of HydraScreen in virtual and high-throughput screening for hit identification in IRAK1. Our platforms and innovative tools can expedite the early stages of drug discovery.

Identification

10.26434/chemrxiv-2023-mh22x-v5

Cite

Citations (1)

Investigating Sulfoxide-to-Sulfone Conversion as a Prodrug Strategy for a Phosphatidylinositol 4-Kinase Inhibitor in a Humanized Mouse Model of Malaria

Antimicrobial Agents and Chemotherapy (2018)

Liezl Gibhard Mathew Njoroge Tanya Paquet Christel Brunschwig Dale Taylor

The in vivo antimalarial efficacies of two phosphatidylinositol 4-kinase (PI4K) inhibitors, a 3,5-diaryl-2-aminopyrazine sulfoxide and its corresponding sulfone metabolite, were evaluated in the NOD-scid IL2Rγ null (NSG) murine malaria disease model of Plasmodium falciparum infection. We hypothesized that the sulfoxide would serve as a more soluble prodrug for the sulfone, which would lead to improved drug exposure with oral dosing.

Sulfone

10.1128/aac.00261-18

Cite

Citations (7)