Abstract
Identification of novel anti-cancer compounds with high efficacy and low toxicity is critical in drug development. High-throughput screening and other such strategies are generally resource-intensive. Therefore, in silico computer-aided drug design has gained rapid acceptance and popularity. We employed our proprietary computational platform (CHEMSAS®), which uses a unique combination of traditional and modern pharmacology principles, statistical modeling, medicinal chemistry, and machine-learning technologies to discover and optimize novel compounds that could target various cancers. COTI-2 is a small molecule candidate anti-cancer drug identified using CHEMSAS. This study describes the in vitro and in vivo evaluation of COTI-2. Our data demonstrate that COTI-2 is effective against a diverse group of human cancer cell lines regardless of their tissue of origin or genetic makeup. Most treated cancer cell lines were sensitive to COTI-2 at nanomolar concentrations. When compared to traditional chemotherapy or targeted-therapy agents, COTI-2 showed superior activity against tumor cells, in vitro and in vivo. Despite its potent anti-tumor efficacy, COTI-2 was safe and well-tolerated in vivo. Although the mechanism of action of COTI-2 is still under investigation, preliminary results indicate that it is not a traditional kinase or an Hsp90 inhibitor.
Highlights
Recent advances in high-throughput cancer genome sequencing such as whole genome sequencing and functional screening using RNA interference have revealed exploitable characteristics of cancer cells that could be targeted in cancer treatment [1]
We tested the efficacy of COTI-2 against a diverse group of human cancer cell lines with different genetic mutation backgrounds
We have developed a proprietary, computerbased drug discovery technology (CHEMSAS) to identify a novel, small molecule, antineoplastic drug candidate called COTI-2
Summary
Recent advances in high-throughput cancer genome sequencing such as whole genome sequencing and functional screening using RNA interference have revealed exploitable characteristics of cancer cells that could be targeted in cancer treatment [1]. Discovering a novel and effective drug can be extremely challenging since not all discovered biological targets are druggable [2]. Developing a drug candidate either through relatively unbiased high-throughput screening (HTS) processes that automate laboratory techniques, or hypothesis-driven research (in the case of molecules targetable by rational design of therapeutic agents), is often challenging and can take years [3]. The advantage of computer-aided drug design over HTS is that, unlike unbiased methods, it is capable of ranking candidate therapeutic compounds to allow selection of a manageably small number for screening in the laboratory [5]. The inclusion of rational elements in the ranking process (for example, selection of the most effective and least toxic structures from existing therapeutic compounds) reduces both time and cost required for preclinical development [6]. Computational technologies that can precisely identify and predict structures with desired inhibitory effects and low toxicity are of utmost value to the modern process of drug development [4]
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