# Novel Dual-Stage Antimalarials: Machine learning prediction, validation and evolution

> **NIH NIH R21** · RUTGERS BIOMEDICAL AND HEALTH SCIENCES · 2024 · $194,915

## Abstract

PROJECT SUMMARY
 Specifically, this proposal focuses on novel new small molecules that inhibit both the blood and liver
stages of malaria infection. The causative pathogen – Plasmodium spp. – was responsible for 241,000,000 cases
that resulted in 627,000 deaths in 2020. Plasmodium spp. drug-resistant infections leave few good choices for
physicians and put at risk the productivity and the lives of those infected. A clear case has been made for new
drugs to treat these infections through the discovery and development of novel therapeutic strategies. These
strategies would optimally be dual stage, targeting the blood stage for treatment and the liver stage for
prophylaxis.
 The innovative strategy in this proposal builds on the technology of machine learning models for the
prediction of novel dual-stage antimalarial small molecules with significant potential as drug discovery entities.
Such a computational approach to seed the discovery of small molecule malaria parasite inhibitors with dual-
stage efficacy has only been reported by us in 2022. The approach begins with preliminary data around two novel
antimalarial small molecules with demonstrated in vitro efficacy versus both blood and liver stages of Plasmodium
spp. infection and a lack of significant cytotoxicity to cultured liver cells. These molecules were derived from a
set of hits discovered with a random forest model trained with high-throughput screening data. The molecules
are representative of novel chemotypes for dual-stage antimalarials and, thus, offer a high probability of
modulating new targets that are critical throughout the parasite’s lifecycle. This initial machine learning effort will
be significantly expanded with a range of model types and a different and larger commercial library to predict a
set of new hit compounds.
 Two validated hits, meeting in vitro efficacy and cytotoxicity criteria and maintaining wild type in vitro
efficacy versus a set of drug-resistant parasite strains, will be profiled for key molecular properties such as mouse
liver microsomal stability, aqueous solubility, and mouse pharmacokinetic profile. These data along with the
existing in vitro efficacy and cytotoxicity evaluations will guide the evolution of each hit with a goal of preparing
one or more analogs with a composite profile to enable downstream in vivo efficacy evaluation in infection
models. A novel combination of medicinal chemistry and machine learning will be leveraged to afford such
molecules.

## Key facts

- **NIH application ID:** 10878908
- **Project number:** 5R21AI174151-02
- **Recipient organization:** RUTGERS BIOMEDICAL AND HEALTH SCIENCES
- **Principal Investigator:** Emily R Derbyshire
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $194,915
- **Award type:** 5
- **Project period:** 2023-07-03 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10878908

## Citation

> US National Institutes of Health, RePORTER application 10878908, Novel Dual-Stage Antimalarials: Machine learning prediction, validation and evolution (5R21AI174151-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10878908. Licensed CC0.

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