Computer‐aided drug design in new druggable targets for the next generation of immune‐oncology therapies

Abstract

all approved immune checkpoint modulators in the clinic are monoclonal antibodies. These have revolutionized cancer therapy because of their remarkable clinical activity. However, their failure to show response in most patients, their required administration by an intravenous injection and the induction of severe immune-related adverse effects, 1-3 constitute some of their important drawbacks.In this context, considerable research effort is being deployed to find the next generation of effective cancer immunotherapeutics. Pioneering efforts include the design of novel immune modulatory small molecules (synthetic and natural products) to specifically target emerging pathways responsible for immune evasion. Small molecules are more affordable and may become invaluable alternatives to monoclonal antibodies. However, small-molecule drug discovery is a complex and challenging multidimensional process, focusing on multiple aspects, as the activity, efficacy, pharmacokinetics, pharmacodynamics, and safety of potential novel candidates. The average length of time from hit discovery to the approval of a new drug is at least 14 years, encompassing the investment of billions of dollars. To circumvent and accelerate this lengthy and costly drug discovery and development process, over the past two decades, drug designers have been exploring computational-aided drug design (CADD) tools to design and identify new drug-like chemical entities that can overcome clinical attrition and lack of efficiency.Revolutionary and accelerated performance hardware architectures, including graphical processor units, innovative algorithms and software advances are now available for high-level computations in a time-affordable way pushing the frontiers of computationally driven drug discovery in the rational search for novel bioactive compounds in modern medicinal chemistry.CADD methodologies can be divided in structure-and ligand-based drug design (SBDD and LBDD). Protein three-dimensional (3D) structures are the basis for structure-based drug design. Once the 3D structure of a protein target is available, the SBDD is usually elected to develop therapeutic agents and to understand the mode of action, as well as, the metabolic process. SBDD embraces several methodologies, namely structure-based virtual screening (SBVS), de novo design or pharmacophore generation. A rational SBDD approach is based on the deep understanding of the key interactions between small molecules (or proteins) and their targets, applying techniques, such as molecular docking and molecular dynamics (Scheme 1).Broadly used for SBVS, molecular docking methods explore the ligand conformations adopted within the binding sites of macromolecular targets (search algorithm) and estimate the ligand-receptor affinities (scoring functions). Molecular docking is nowadays one of the elected methods to use in SBVS with a huge rate of success. However, molecular docking works on rigid structures forgetting structural flexibility and structural rearrangements. Although this is a generally ...

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

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Antimicrobial activity of Rhus Coriaria L. and Salvia Urmiensis bunge against some food-borne pathogens and identification of active components using molecular networking and docking analyses

“…Among these new approaches, molecular networking (MN) is a particularly effective one to dereplicate the chemical components by providing the MS/MS fragmentation spectra of samples and compare them with databases (Allard et al, 2016). Today, computer-aided drug discovery (CADD) tools are also getting a lot of attention in the pharmaceutical industry and academia (Acúrcio et al, 2019). CADD has played an important role in the discovery of several pharmaceutical drugs that have obtained FDA approvals (Sliwoski et al, 2013).…”