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Review
. 2021 Aug;1876(1):188572.
doi: 10.1016/j.bbcan.2021.188572. Epub 2021 May 31.

The case for AI-driven cancer clinical trials - The efficacy arm in silico

Likhitha Kolla  1 , Fred K Gruber  2 , Omar Khalid  2 , Colin Hill  3 , Ravi B Parikh  1
Affiliations
Review

The case for AI-driven cancer clinical trials - The efficacy arm in silico

Likhitha Kolla et al. Biochim Biophys Acta Rev Cancer. 2021 Aug.

Abstract

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine.

Keywords: AI; Causal AI; Disease modeling; Efficacy arm; Systems biomedicine; in silico clinical trials.

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Conflict of interest statement

Declaration of Competing Interest

The authors declare that they have the following competing financial interests or personal relationships: RBP has received personal fees and equity from GNS Healthcare. FG and CH are employees of GNS Healthcare.

Figures

Figure 1.
Figure 1.
Fragment of causal network describing the relationship between variables in the CoMMPass IA9 dataset (Furchtgott et al. 2017). This network structure estimates the effect of interventions by simulating how changes in values of variables affects other variables downstream in the networks. Orange are genes, yellow are laboratories, gray are demographics, white are somatic variants, and green are treatment variables. The size of the nodes is relative to the estimated size of the effect from simulations, the width of the edges represents the confidence in the connection and the color represent whether the relationship between variables is direct or inverse.
Figure 2.
Figure 2.
An independent non-prospective clinical trial dataset (DFCI) confirms a sub-population of responder and non-responder to SCT stratified by CHEK1. Patients with high expression of CHEK1 appeared to have a smaller stem cell transplant effect on PFS.
See this image and copyright information in PMC

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