Uncovering the Mechanism of Action of Evolocumab against PAD

Understanding the mechanism of action (MoA) of a drug is a cornerstone in drug development, offering invaluable insights that drive pharmaceutical discovery. For drug discovery, deciphering which targets and pathways a drug affects can inform on therapeutic efficacy and forecasting of potential side effects. This understanding is a strategic imperative, shaping the development of more-targeted and effective therapeutics and reducing the likelihood of late-stage failures.

In this use case, we leveraged Causaly to first explore treatments for peripheral arterial disease (PAD) investigated in clinical trials in the last 5 years, uncovering monoclonal antibody, evolocumab, as a drug of interest. Then, to better understand the drug-disease relationship, we used Causaly’s hypothesis generation tool to explore potential mediators of the effect of evolocumab on PAD.

Using Causaly, treatments of a disease can be rapidly identified. Over 3,700 therapeutic approaches to PAD and related diseases were identified from the ClinicalTrials.gov database. More than half of these treatments have been studied in clinical trials since 2018. As PAD mainly affects the cardiovascular system, we focused on cardiovascular system drugs, uncovering 125 drugs studied in clinical trials.

Examination of the supporting evidence uncovered that evolocumab – a cholesterol-lowering drug has been investigated in clinical trials for patients with PAD: a clinical trial is investigating the effect of evolocumab on functional status and LDL oxidation in PAD patients who are already on a stable, maximal tolerated lipid-lowering regimen with a statin.¹ In this use case, we selected evolocumab as a drug of interest to further explore.

Using Causaly, the potential mediators of the effect of a drug against a disease can be determined. This enables researchers to breakdown the drug-disease relationship, better understand MoAs and identify breakthrough targets or signaling cascades. In this use case, around 40 genes and proteins were identified as potential mediators the effect of evolocumab against PAD, as illustrated in Figure 1. As example, we selected proprotein convertase subtilisin/kexin type 9 (PCSK9) and apolipoprotein B (ApoB) as possible mediators of evolocumab against PAD to further investigate.

PCSK9: It is well-established that evolocumab downregulates the expression of PCSK9,² with almost 400 pieces of evidence from ~1,500 articles extracted by Causaly. The loss of function of PCSK9 has also been reported to have a protective role against atherosclerotic PAD,³ suggesting an upregulated gene-disease relationship. These findings could form the starting point for hypothesis generation.

ApoB: Around 40 pieces of evidence for a relationship between evolocumab and ApoB were uncovered using Causaly Cloud. A 2021 study showed that T2DM patients treated with evolocumab effectively reduced ApoB.⁴ This protein has also been linked to PAD, with a recent study reporting ApoB as a causal lipoprotein-related risk factor for PAD.⁵ Findings from 2022 also support the hypothesis that ApoB, rather than LDL cholesterol and triglycerides, is responsible for causing PAD in people with hyperlipidemia.⁶

Deciphering a drug’s MoA is key to rationalizing findings and anticipating potential safety concerns in early drug discovery. AI can revolutionize this process by facilitating knowledge discovery without bias, unveiling hidden drug-disease interactions. Such insights can serve as a starting point for hypotheses, driving more informed and strategic research directions in the quest for effective therapeutics.