Finding inhibitors for PCSK9 using computational methods

PCSK9 inhibitors as safer therapeutics for atherosclerotic cardiovascular disease (ASCVD): Pharmacophore design and molecular dynamics analysis

Cardiovascular disorders are still challenging and are among the deadly diseases. As a major risk factor for atherosclerotic cardiovascular disease, dyslipidemia, and high low-density lipoprotein cholesterol in particular, can be prevented primary and secondary by lipid-lowering medications. Therefore, insights are still needed into designing new drugs with minimal side effects. Proprotein convertase subtilisin/kexin 9 (PCSK9) enzyme catalyses protein-protein interactions with low-density lipoprotein, making it a critical target for designing promising inhibitors compared to statins. Therefore, we screened for potential compounds using a redesigned PCSK9 conformational behaviour to search for a significantly extensive chemical library and investigated the inhibitory mechanisms of the final compounds using integrated computational methods, from ligand essential functional group screening to all-atoms MD simulations and MMGBSA-based binding free energy. The inhibitory mechanisms of the screened compounds compared with the standard inhibitor. K31 and K34 molecules showed stronger interactions for PCSK9, having binding energy (kcal/mol) of -33.39 and -63.51, respectively, against -27.97 of control. The final molecules showed suitable drug-likeness, non-mutagenesis, permeability, and high solubility values. The C-α atoms root mean square deviation and root mean square fluctuation of the bound-PCSK9 complexes showed stable and lower fluctuations compared to apo PCSK9. The findings present a model that unravels the mechanism by which the final molecules proposedly inhibit the PCSK9 function and could further improve the design of novel drugs against cardiovascular diseases.

Comprehensive computational study in the identification of novel potential cholesterol lowering agents targeting proprotein convertase subtilisin/kexin type 9

The enzymatic target proprotein convertase subtilisin/kexin type 9 (PCSK9) is critically involved in the regulation of the lipoprotein metabolism leading to the degradation of low-density lipoprotein receptors (LDLRs) upon binding. Drugs that lower LDL cholesterol (LDL-C) through the inhibition of PCSK9 are useful in the management of hypercholesterolemia which greatly reduces the associated risk of atherosclerotic cardiovascular disease (CVD). In 2015, anti-PCSK9 monoclonal antibodies (mAbs), alirocumab and evolocumab were approved but owing to their high costs their prior authorization practices were impeded, reducing their long-term adherence. This has drawn considerable attention for the development of small-molecule PCSK9 inhibitors. In this research work, novel and diverse molecules with affinity towards PCSK9 thereby having ability to lower cholesterol. A hierarchical multistep docking was implemented to identify small molecules from chemical libraries with a score cutoff -8.00 kcal/mol, thereby weeding all the non-potential molecules. A set of seven representative molecules Z1139749023, Z1142698190, Z2242867634, Z2242893449, Z2242894417, Z2242909019, and Z2242914794 have been identified from a comprehensive computational study which included assessment of pharmacokinetics and toxicity profiles and binding interactions along with in-depth analysis of structural dynamics and integrity using prolong molecular dynamics (MD) simulation (in-duplicate). Furthermore the binding affinity of these PCSK9 inhibitory candidates molecules was ascertained over 1000 trajectory frames using MM-GBSA calculations. The molecules reported herein are propitious candidates for further development through necessary experimental considerations.Communicated by Ramaswamy H. Sarma.

Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models

The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.

Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases

Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.

Brazilin from Caesalpinia sappan L. as a Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Inhibitor: Pharmacophore-Based Virtual Screening, In Silico Molecular Docking, and In Vitro Studies

Background

Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a crucial regulator of low-density lipoprotein cholesterol (LDL-c) levels, as it binds to and degrades the LDL receptor (LDLR) in the lysosome of hepatocytes. Elevated levels of PCSK9 have been linked to an increased LDL-c plasma levels, thereby increasing the risk of cardiovascular disease (CVD), making it an attractive target for therapeutic interventions. As a way to inhibit PCSK9 action, we searched for naturally derived small molecules which can block the binding of PCSK9 to the LDLR.

Methods

In this study, we carried out in silico studies which consist of virtual screening using an optimized pharmacophore model and molecular docking studies using Pyrx 0.98. Effects of the candidate compounds were evaluated using in vitro PCSK9-LDLR binding assays kit.

Results

Eleven natural compounds that bind to PCSK9 were virtually screened form HerbalDB database, including brazilin. Next, molecular docking studies using Pyrx 0.98 showed that brazilin had the highest binding affinity with PCSK9 at -9.0 (Kcal/mol), which was higher than that of the other ten compounds. Subsequent in vitro PCSK9-LDLR binding assays established that brazilin decreased the binding of PCSK9 to the EGF-A fragment of the LDLR in a dose-dependent manner, with an IC50 value of 2.19 μM.

Conclusion

We have identified brazilin, which is derived from the Caesalpinia sappan herb, which can act as a small molecule inhibitor of PCSK9. Our findings suggest that screening for small molecules that can block the interaction between PCSK9 and the LDLR in silico and in vitro may be a promising approach for developing novel lipid-lowering therapy.

In Silico Identification of New Anti-SARS-CoV-2 Main Protease (Mpro) Molecules with Pharmacokinetic Properties from Natural Sources Using Molecular Dynamics (MD) Simulations and Hierarchical Virtual Screening

Infectious agents such as SARS-CoV, MERS-CoV, and SARS-CoV-2 have emerged in recent years causing epidemics with high mortality rates. The quick development of novel therapeutic compounds is required in the fight against such pathogenic agents. Unfortunately, the traditional drug development methods are time-consuming and expensive. In this study, computational algorithms were utilized for virtual screening of a library of natural compounds in the ZINC database for their affinity towards SARS-CoV-2 Mpro. Compounds such as cinanserin, nelfinavir, baicalin, baicalein, candesartan cilexetil, chloroquine, dipyridamole, and hydroxychloroquine have the ability to prevent SARS-CoV-2 Mpro from facilitating COVID 19 infection; thus, they treat COVID 19. However, these drugs majorly act to reduce the symptoms of the disease. No anti-viral drug against COVID 19 virus infection has been discovered and approved. Therefore, this study sought to explore natural inhibitors of SARS-CoV-2 Mpro to develop a pharmacophore model for virtual screening of natural compounds in the ZINC database as potential candidates for SARS-CoV-2 Mpro inhibitors and as therapeutic molecules against COVID 19. This study undertook in silico methods to identify the best anti-viral candidates targeting SAR-CoV-2 Mpro from natural sources in the ZINC database. Initially, reported anti-SARS-CoV-2 Mpro molecules were integrated into designing a pharmacophore model utilizing PharmaGist. Later, the pharmacophore model was loaded into ZINCPHARMER and screened against the ZINC database to identify new probable drug candidates. The root means square deviation (RMSD) values of the potential drug candidates informed the selection of some of them, which were docked with SARS-CoV-2 Mpro to comprehend their interactions. From the molecular docking results, the top four candidates (ZINC000254823011, ZINC000072307130, ZINC000013627512, and ZINC000009418994) against SARS-CoV-2 Mpro, with binding energies ranging from –8.2 kcal/mol to –8.6 kcal/mol, were examined for their oral bioavailability and other pharmacokinetic properties. Consequently, ZINC000072307130 emerged as the only orally bioavailable drug candidate with desirable pharmacokinetic properties. This candidate drug was used to perform MD simulations, and the outcomes revealed that ZINC000072307130 formed a stable complex with the viral main protease. Consequently, ZINC000072307130 emerges as a potential anti-SARS-CoV-2 Mpro inhibitor for the production of new COVID 19 drugs.

The Cholesterol-Modulating Effect of the New Herbal Medicinal Recipe from Yellow Vine (Coscinium fenestratum (Goetgh.)), Ginger (Zingiber officinale Roscoe.), and Safflower (Carthamus tinctorius L.) on Suppressing PCSK9 Expression to Upregulate LDLR Expression in HepG2 Cells

PCSK9 is a promising target for developing novel cholesterol-lowering drugs. We developed a recipe that combined molecular docking, GC-MS/MS, and real-time PCR to identify potential PCSK9 inhibitors for herb ratio determination. Three herbs, Carthamus tinctorius, Coscinium fenestratum, and Zingiber officinale, were used in this study. This work aimed to evaluate cholesterol-lowering through a PCSK9 inhibitory mechanism of these three herbs for defining a suitable ratio. Chemical constituents were identified using GC-MS/MS. The PCSK9 inhibitory potential of the compounds was determined using molecular docking, real-time PCR, and Oil red O staining. It has been shown that most of the active compounds of C. fenestratum and Z. officinale inhibit PCSK9 when extracted with water, and C. fenestratum has been shown to yield tetraacetyl-d-xylonic nitrile (27.92%) and inositol, 1-deoxy-(24.89%). These compounds could inhibit PCSK9 through the binding of 6 and 5 hydrogen bonds, respectively, while the active compound in Z. officinale is 2-Formyl-9-[.beta.-d-ribofuranosyl] hypoxanthine (4.37%) inhibits PCSK9 by forming 8 hydrogen bonds. These results suggest that a recipe comprising three parts C. fenestratum, two parts Z. officinale, and one part C. tinctorius is a suitable herbal ratio for reducing lipid levels in the bloodstream through a PCSK9 inhibitory mechanism.

A mathematical model of in vitro hepatocellular cholesterol and lipoprotein metabolism for hyperlipidemia therapy

Cardiovascular diseases associated with high cholesterol (hypercholesterolemia) and low-density lipoproteins (LDL) levels are significant contributors to total mortality in developing and developed countries. Mathematical modeling of LDL metabolism is an important step in the development of drugs for hypercholesterolemia. The aim of this work was to develop and to analyze an integrated mathematical model of cholesterol metabolism in liver cells and its interaction with two types of drugs, statins and PCSK9 inhibitors. The model consisted of 21 ordinary differential equations (ODE) describing cholesterol biosynthesis and lipoprotein endocytosis in liver cells in vitro. The model was tested for its ability to mimic known biochemical effects of familial hypercholesterolemia, statin therapy, and PCSK9 inhibitors. The model qualitatively reproduced the well-known biology of cholesterol regulation, which confirms its potential for minimizing cellular research in initial testing of new drugs for cardiology.

Biological Therapies in the Treatment of Cancer-Update and New Directions

Biological therapies have changed the face of oncology by targeting cancerous cells while reducing the effect on normal tissue. This publication focuses mainly on new therapies that have contributed to the advances in treatment of certain malignancies. Immunotherapy, which has repeatedly proven to be a breakthrough therapy in melanoma, as well as B-ALL therapy with CAR T cells, are of great merit in this progress. These therapies are currently being developed by modifying bispecific antibodies and CAR T cells to improve their efficiency and bioavailability. Work on improving the therapy with oncolytic viruses is also progressing, and efforts are being made to improve the immunogenicity and stability of cancer vaccines. Combining various biological therapies, immunotherapy with oncolytic viruses or cancer vaccines is gaining importance in cancer therapy. New therapeutic targets are intensively sought among neoantigens, which are not immunocompromised, or antigens associated with tumor stroma cells. An example is fibroblast activation protein α (FAPα), the overexpression of which is observed in the case of tumor progression. Universal therapeutic targets are also sought, such as the neurotrophic receptor tyrosine kinase (NTRK) gene fusion, a key genetic driver present in many types of cancer. This review also raises the problem of the tumor microenvironment. Stromal cells can protect tumor cells from chemotherapy and contribute to relapse and progression. This publication also addresses the problem of cancer stem cells resistance to treatment and presents attempts to avoid this phenomenon. This review focuses on the most important strategies used to improve the selectivity of biological therapies.