Malaria PGx Atlas: Causal-Inference-Linked Clinical–Parasite–Host Multi-omics with CRISPR Panels for Predictive Therapy

Amar et al. (2023) show in oncology how causal inference on multi-center clinical datasets, coupled with a high-throughput in vivo PGx platform, can robustly validate genomic biomarkers of drug response. Antimalarial discovery urgently needs such a bridge (Siqueira-Neto et al., 2023; Schäfer et al., 2023). This project assembles: (i) clinical cohorts with treatment histories (ACT regimens, failures), (ii) matched parasite genomes (k13, pfcrt, pfmdr1, pfubp1, etc.) and host genotypes (e.g., G6PD status), and (iii) CRISPR-edited P. falciparum panels that systematically introduce resistance and compensatory alleles. Doubly robust causal estimators (as in Amar et al.) correct for time-varying treatments and immortal time bias to infer which parasite–host variant combinations drive failure for specific regimens. Parallel PK/PD modeling (Bakshi et al., 2013) defines exposure requirements by genotype and stage. The deliverable is a public “PGx Atlas” that predicts optimal combinations and dosing schedules by locale-specific genotypes, and flags candidates with low resistance propensity (Cheuka et al., 2024). This goes beyond current reviews by operationalizing precision antimalarial therapy and guiding portfolio prioritization with real-world and engineered-evidence loops.

References:

1. Model System to Define Pharmacokinetic Requirements for Antimalarial Drug Efficacy. R. Bakshi, E. Nenortas, A. Tripathi, D. Sullivan, T. Shapiro (2013). Science Translational Medicine.
2. Emerging Drug Targets for Antimalarial Drug Discovery: Validation and Insights into Molecular Mechanisms of Function.. P. Cheuka, Paul Njaria, G. Mayoka, Evelyn Funjika (2024). Journal of Medicinal Chemistry.
3. Antimalarial drug discovery: progress and approaches. Jair L. Siqueira-Neto, Kathryn J. Wicht, K. Chibale, J. Burrows, D. Fidock, E. Winzeler (2023). Nature reviews. Drug discovery.
4. The problem of antimalarial resistance and its implications for drug discovery. Thomas Martin Schäfer, Lais Pessanha de Carvalho, Juliana Inoue, A. Kreidenweiss, Jana Held (2023). Expert Opinion on Drug Discovery.
5. Abstract 5723: An in vivo pharmacogenomics platform replicates and extends biomarkers of therapy response identified via causal inference analysis of clinical data. D. Amar, Erick Scott, Ian P. Winters, Gregory D. Wall, D. Petrov, M. Winslow, Joseph M. Juan, Ian Lai, Lafia Sebastian, Edwin A. Apilado, Gabriel Grenot, Vy B. Tran, C. Rudin, M. Rosen (2023). Cancer Research.