Translating atherosclerosis research from bench to bedside: navigating the barriers for effective preclinical drug discovery

Abstract

Cardiovascular disease (CVD) remains the leading cause of death worldwide. An ongoing challenge remains the development of novel pharmacotherapies to treat CVD, particularly atherosclerosis. Effective mechanism-informed development and translation of new drugs requires a deep understanding of the known and currently unknown biological mechanisms underpinning atherosclerosis, accompanied by optimization of traditional drug discovery approaches. Current animal models do not precisely recapitulate the pathobiology underpinning human CVD. Accordingly, a fundamental limitation in early-stage drug discovery has been the lack of consensus regarding an appropriate experimental in vivo model that can mimic human atherosclerosis. However, when coupled with a clear understanding of the specific advantages and limitations of the model employed, preclinical animal models remain a crucial component for evaluating pharmacological interventions. Within this perspective, we will provide an overview of the mechanisms and modalities of atherosclerotic drugs, including those in the preclinical and early clinical development stage. Additionally, we highlight recent preclinical models that have improved our understanding of atherosclerosis and associated clinical consequences and propose model adaptations to facilitate the development of new and effective treatments.

Design, Synthesis, and Anti-Inflammatory Evaluation of a Novel PPARδ Agonist with a 4-(1-Pyrrolidinyl)piperidine Structure

“…1 H NMR (400 MHz, DMSO-d 6 ): δ 0.94 (d, J = 6.5 Hz, 6H), 1.35 (d,J = 5.1 Hz,2H), 4H),1H),1.91 (t,J = 7.0 Hz,2H),3H),3.56 (s,2H), 4.03 (t, J = 7.0 Hz, 2H), 6.83 (br s, 1H), 6.90 (br s, 1H), 6.95 (dd, J = 2.0, 2.0 Hz, 1H), 7.27 (dd,J = 8.7,2.3 Hz,1H),7.40 (d,J = 8.7 Hz,1H),7.87 (d,J = 2.3 Hz,1H),12.42 (br s,1H). 13 C NMR (100 MHz, DMSO-d 6 ): δ 23.00, 25.29, 33.75, 33.91, 34.48, 40.06, 44.32, 45.27, 64.44, 112.46, 115.01, 119.19, 120.72, 121.53, 124.56, 126.03, 131.92, 133.30, 138.19, 151.60, 159.25, 168.37, 172.11 2- piperidin-4yl)ethoxy)-5-chlorophenyl)acetic Acid (7). Compound 7 was prepared from 32d in a similar manner to 2.…”

“…Next, we investigated the effect of branched chains on the activity and selectivity. Isopropyl ( 5), isobutyl (6), and secbutyl (7) analogues showed lower EC 50 values for hPPARδ (5: EC 50 = 7.5 nM, 6: EC 50 = 5.9 nM, 7: EC 50 = 4.5 nM) than corresponding compounds having linear alkyl groups and had good subtype selectivity from the viewpoint of E max values in comparison with PPARα/γ. In particular, 7 with a sec-butyl group demonstrated the lowest EC 50 value of PPARδ among the compounds described above.…”

“…5 Anti-inflammatory agents such as canakinumab (anti-IL-1β antibody), methotrexate, and colchicine have been tried in clinical trials for arteriosclerosis, but without sufficient success. 6,7 Peroxisome proliferator-activated receptors (PPARs) are ligand-activated transcription factors belonging to the nuclear receptor superfamily that regulate energy balance by influencing the metabolism of lipids and glucose. 8,9 There are three PPAR isoforms, PPARα, PPARγ, and PPARδ (also called PPARβ or PPARβ/δ).…”

“…Now it is evident that atherosclerosis is a chronic inflammatory disease which is induced by various risk factors such as dyslipidemia and is closely related to the effects of proinflammatory and anti-inflammatory cytokines/chemokines . Anti-inflammatory agents such as canakinumab (anti-IL-1β antibody), methotrexate, and colchicine have been tried in clinical trials for arteriosclerosis, but without sufficient success. , …”

ChatGPT or LLM in next-generation drug discovery and development: Pharmaceutical and biotechnology companies can make use of the artificial intelligence (AI)-based device for a faster way of drug discovery and development

“…The high-cost results from the need for expensive equipment, facilities, and personnel. An additional essential constraint in the initial stages of drug exploration has been the absence of unanimity concerning a suitable in vivo experimental model that can effectively imitate human ailments 10 . Recently, Sharma and Thakur illustrated that AI-assisted ChatGPT performs various steps of drug discovery, such as calculating the compound multiplicity, file conversion input file generation for Gaussian software, searching the pdb files, input file generation for docking, etc.…”