Discovery of Targeted Covalent Natural Products against PLK1 by Herb-Based Screening

Integrating Phenotypic and Chemoproteomic Approaches to Identify Covalent Targets of Dietary Electrophiles in Platelets

A large variety of dietary phytochemicals has been shown to improve thrombosis and stroke outcomes in preclinical studies. Many of these compounds feature electrophilic functionalities that potentially undergo covalent addition to the sulfhydryl side chain of cysteine residues within proteins. However, the impact of such covalent modifications on the platelet activity and function remains unclear. This study explores the irreversible engagement of 23 electrophilic phytochemicals with platelets, unveiling the unique antiplatelet selectivity of sulforaphane (SFN). SFN impairs platelet responses to adenosine diphosphate (ADP) and a thromboxane A2 receptor agonist while not affecting thrombin and collagen-related peptide activation. It also substantially reduces platelet thrombus formation under arterial flow conditions. Using an alkyne-integrated probe, protein disulfide isomerase A6 (PDIA6) was identified as a rapid kinetic responder to SFN. Mechanistic profiling studies revealed SFN's nuanced modulation of PDIA6 activity and substrate specificity. In an electrolytic injury model of thrombosis, SFN enhanced the thrombolytic activity of recombinant tissue plasminogen activator (rtPA) without increasing blood loss. Our results serve as a catalyst for further investigations into the preventive and therapeutic mechanisms of dietary antiplatelets, aiming to enhance the clot-busting power of rtPA, currently the only approved therapeutic for stroke recanalization that has significant limitations.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Computer Science Technology in Natural Products Research: A Review of Its Applications and Implications

Computational approaches to drug development are rapidly growing in popularity and have been used to produce significant results. Recent developments in information science have expanded databases and chemical informatics knowledge relating to natural products. Natural products have long been well-studied, and a large number of unique structures and remarkable active substances have been reported. Analyzing accumulated natural product knowledge using emerging computational science techniques is expected to yield more new discoveries. In this article, we discuss the current state of natural product research using machine learning. The basic concepts and frameworks of machine learning are summarized. Natural product research that utilizes machine learning is described in terms of the exploration of active compounds, automatic compound design, and application to spectral data. In addition, efforts to develop drugs for intractable diseases will be addressed. Lastly, we discuss key considerations for applying machine learning in this field. This paper aims to promote progress in natural product research by presenting the current state of computational science and chemoinformatics approaches in terms of its applications, strengths, limitations, and implications for the field.

Polo-like Kinase 1 Inhibitors in Human Cancer Therapy: Development and Therapeutic Potential

Polo-like kinase 1 (PLK1) plays an important role in a variety of cellular functions, including the regulation of mitosis, DNA replication, autophagy, and the epithelial-mesenchymal transition (EMT). PLK1 overexpression is often associated with cell proliferation and poor prognosis in cancer patients, making it a promising antitumor target. To date, at least 10 PLK1 inhibitors (PLK1i) have been entered into clinical trials, among which the typical kinase domain (KD) inhibitor BI 6727 (volasertib) was granted "breakthrough therapy designation" by the FDA in 2013. Unfortunately, many other KD inhibitors showed poor specificity, resulting in dose-limiting toxicity, which has greatly impeded their development. Researchers recently discovered many PLK1i with higher selectivity, stronger potency, and better absorption, distribution, metabolism, and elimination (ADME) characteristics. In this review, we emphasize the structure-activity relationships (SARs) of PLK1i, providing insights into new drugs targeting PLK1 for antitumor clinical practice.

Anti-tumor pharmacology of natural products targeting mitosis

Cancer has been an insurmountable problem in the history of medical science. The uncontrollable proliferation of cancer cells is one of cancer’s main characteristics, which is closely associated with abnormal mitosis. Targeting mitosis is an effective method for cancer treatment. This review summarizes several natural products with anti-tumor effects related to mitosis, focusing on targeting microtubulin, inducing DNA damage, and modulating mitosis-associated kinases. Furthermore, the main disadvantages of several typical compounds, including drug resistance, toxicity to non-tumor tissues, and poor aqueous solubility and pharmacokinetic properties, are also discussed, together with strategies to address them. Improved understanding of cancer cell mitosis and natural products may pave the way to drug development for the treatment of cancer.

Fundamental considerations in drug design

The drug discovery paradigm has been very time-consuming, challenging, and expensive; however, the disease conditions originating from bacteria, virus, protozoa, fungus and other microorganisms are steadily shooting up. For instance, COVID-19 is the latest viral infection that affects millions of people and the world’s economy very severely. Therefore, the quest for discovery of novel and potent drug compounds against deadly pathogens is crucial at the moment. Despite a lot of drawbacks in drug discovery and development and its pertaining technology, the advancement must be taken into account so the time duration and cost would be minimized. In this chapter, basic principles in drug design and discovery have been discussed together with advances in drug development.

Contemporary Approaches to the Discovery and Development of Broad-Spectrum Natural Product Prototypes for the Control of Coronaviruses

The pressing need for SARS-CoV-2 controls has led to a reassessment of strategies to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. This review article addresses how contemporary approaches involving computational chemistry, natural product (NP) and protein databases, and mass spectrometry (MS) derived target-ligand interaction analysis can be utilized to expedite the interrogation of NP structures while minimizing the time and expense of extraction, purification, and screening in BioSafety Laboratories (BSL)3 laboratories. The unparalleled structural diversity and complexity of NPs is an extraordinary resource for the discovery and development of broad-spectrum inhibitors of viral genera, including Betacoronavirus, which contains MERS, SARS, SARS-CoV-2, and the common cold. There are two key technological advances that have created unique opportunities for the identification of NP prototypes with greater efficiency: (1) the application of structural databases for NPs and target proteins and (2) the application of modern MS techniques to assess protein-ligand interactions directly from NP extracts. These approaches, developed over years, now allow for the identification and isolation of unique antiviral ligands without the immediate need for BSL3 facilities. Overall, the goal is to improve the success rate of NP-based screening by focusing resources on source materials with a higher likelihood of success, while simultaneously providing opportunities for the discovery of novel ligands to selectively target proteins involved in viral infection.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

One Century of Study: What We Learned about Paracoccidioides and How This Pathogen Contributed to Advances in Antifungal Therapy

Paracoccidioidomycosis (PCM) is a notable fungal infection restricted to Latin America. Since the first description of the disease by Lutz up to the present day, Brazilian researchers have contributed to the understanding of the life cycle of this pathogen and provided the possibility of new targets for antifungal therapy based on the structural and functional genomics of Paracoccidioides. In this context, in silico approaches have selected molecules that act on specific targets, such as the thioredoxin system, with promising antifungal activity against Paracoccidioides. Some of these are already in advanced development stages. In addition, the application of nanostructured systems has addressed issues related to the high toxicity of conventional PCM therapy. Thus, the contribution of molecular biology and biotechnology to the advances achieved is unquestionable. However, it is still necessary to transcend the boundaries of synthetic chemistry, pharmaco-technics, and pharmacodynamics, aiming to turn promising molecules into newly available drugs for the treatment of fungal diseases.