Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.     

Molecular docking analysis of FDA approved drugs with the glycoprotein from Junin and Machupo viruses

Arenaviruses, Junin and Machupo are pathogenic viruses in regions of South America including Argentina and Bolivia causing haemorrhagic fever among humans. They have been transmitted to humans through mouse causing chronic illness with high mortality. Therefore, it is of interest to acquittance the molecular docking analysis data of FDA approved drugs with the glycoprotein from Junin and Machupo viruses for consideration in drug discovery. Thus, we report the optimal binding features of MK-3207 and Dihydro ergotamine with the protein target for further validation and consideration.     

Cross‐docking benchmark for automated pose and ranking prediction of ligand binding

Significant efforts have been devoted in the last decade to improving molecular docking techniques to predict both accurate binding poses and ranking affinities. Some shortcomings in the field are the limited number of standard methods for measuring docking success and the availability of widely accepted standard data sets for use as benchmarks in comparing different docking algorithms throughout the field. In order to address these issues, we have created a Cross‐Docking Benchmark server. The server is a versatile cross‐docking data set containing 4,399 protein‐ligand complexes across 95 protein targets intended to serve as benchmark set and gold standard for state‐of‐the‐art pose and ranking prediction in easy, medium, hard, or very hard docking targets. The benchmark along with a customizable cross‐docking data set generation tool is available at http://disco.csb.pitt.edu . We further demonstrate the potential uses of the server in questions outside of basic benchmarking such as the selection of the ideal docking reference structure.     

New pharmaceuticals approved by FDA in 2020: Small-molecule drugs derived from amino acids and related compounds

Amino acids (AAs) play an important role in the modern health industry as key synthetic precursors for pharmaceuticals, biomaterials, biosensors, and drug delivery systems. Currently, over 30% of small-molecule drugs contain residues of tailor-made AAs or derived from them amino-alcohols and di-amines. In this review article, we profile 12 AA-derived new pharmaceuticals approved by the FDA in 2020. These newly introduced drugs include Tazverik (epithelioid sarcoma), Gemtesa (overactive bladder), Zeposia (multiple sclerosis), Byfavo (induction and maintenance of procedural sedation), Cu 64 dotatate, and Gallium 68 PSMA-11 (both PET imaging), Rimegepant (acute migraine), Zepzelca (lung cancer), Remdesivir (COVID-19), Amisulpride (nausea and vomiting), Setmelanotide (obesity), and Lonafarnib (progeria syndrome). For each compound, we describe the spectrum of biological activity, medicinal chemistry discovery, and synthetic preparation.     

Automatic prediction of flexible regions improves the accuracy of protein-protein docking models

Computational models of protein-protein docking that incorporate backbone flexibility can predict perturbations of the backbone and side chains during docking and produce protein interaction models with atomic accuracy. Most previous models usually predefine flexible regions by visually comparing the bound and unbound structures. In this paper, we propose a general method to automatically identify the flexible hinges for domain assembly and the flexible loops for loop refinement, in addition to predicting the corresponding movements of the identified active residues. We conduct experiments to evaluate performance of our approach on two test sets. Comparison of results on test set I between algorithms with and without prediction of flexible regions demonstrate the superior recovery of energy funnels in many target interactions using the new loop refinement model. In addition, our decoys are superior for each target. Indeed, the total number of satisfactory models is almost double that of other programs. The results on test set II docking tests produced by our domain assembly method also show encouraging results. Of the three targets examined, one exhibits energy funnel and the best models of the other two targets all meet the conditions of acceptable accuracy. Results demonstrate that the automatic prediction of flexible backbone regions can greatly improve the performance of protein-protein docking models.     

Network-assisted investigation of antipsychotic drugs and their targets

Antipsychotic drugs are tranquilizing psychiatric medications primarily used in the treatment of schizophrenia and similar severe mental disorders. So far, most of these drugs have been discovered without knowing much on the molecular mechanisms of their actions. The available large amount of pharmacogenetics, pharmacometabolomics, and pharmacoproteomics data for many drugs makes it possible to systematically explore the molecular mechanisms underlying drug actions. In this study, we applied a unique network-based approach to investigate antipsychotic drugs and their targets. We first retrieved 43 antipsychotic drugs, 42 unique target genes, and 46 adverse drug interactions from the DrugBank database and then generated a drug-gene network and a drug-drug interaction network. Through drug-gene network analysis, we found that seven atypical antipsychotic drugs tended to form two clusters that could be defined by drugs with different target receptor profiles. In the drug-drug interaction network, we found that three drugs (zuclopenthixol, ziprasidone, and thiothixene) tended to have more adverse drug interactions than others, while clozapine had fewer adverse drug interactions. This investigation indicated that these antipsychotics might have different molecular mechanisms underlying the drug actions. This pilot network-assisted investigation of antipsychotics demonstrates that network-based analysis is useful for uncovering the molecular actions of antipsychotics.     

An NMR-based scoring function improves the accuracy of binding pose predictions by docking by two orders of magnitude

Low-affinity ligands can be efficiently optimized into high-affinity drug leads by structure based drug design when atomic-resolution structural information on the protein/ligand complexes is available. In this work we show that the use of a few, easily obtainable, experimental restraints improves the accuracy of the docking experiments by two orders of magnitude. The experimental data are measured in nuclear magnetic resonance spectra and consist of protein-mediated NOEs between two competitively binding ligands. The methodology can be widely applied as the data are readily obtained for low-affinity ligands in the presence of non-labelled receptor at low concentration. The experimental inter-ligand NOEs are efficiently used to filter and rank complex model structures that have been pre-selected by docking protocols. This approach dramatically reduces the degeneracy and inaccuracy of the chosen model in docking experiments, is robust with respect to inaccuracy of the structural model used to represent the free receptor and is suitable for high-throughput docking campaigns.     

Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges

Tremendous efforts have been made over the past few decades to discover novel cancer biomarkers for use in clinical practice. However, a striking discrepancy exists between the effort directed toward biomarker discovery and the number of markers that make it into clinical practice. One of the confounding issues in translating a novel discovery into clinical practice is that quite often the scientists working on biomarker discovery have limited knowledge of the analytical, diagnostic, and regulatory requirements for a clinical assay. This review provides an introduction to such considerations with the aim of generating more extensive discussion for study design, assay performance, and regulatory approval in the process of translating new proteomic biomarkers from discovery into cancer diagnostics. We first describe the analytical requirements for a robust clinical biomarker assay, including concepts of precision, trueness, specificity and analytical interference, and carryover. We next introduce the clinical considerations of diagnostic accuracy, receiver operating characteristic analysis, positive and negative predictive values, and clinical utility. We finish the review by describing components of the FDA approval process for protein-based biomarkers, including classification of biomarker assays as medical devices, analytical and clinical performance requirements, and the approval process workflow. While we recognize that the road from biomarker discovery, validation, and regulatory approval to the translation into the clinical setting could be long and difficult, the reward for patients, clinicians and scientists could be rather significant.     

Derivative emission spectrofluorimetry: Application to the analysis of newly approved FDA combination of ibuprofen and famotidine in tablets

A new combination of ibuprofen (NSAID) and famotidine (H₂ receptor antagonist) was recently approved by the FDA. It was formulated to relief pain while decreasing the risk of ulceration, which is a common problem for patients receiving NSAID. A rapid and simple derivative emission spectrofluorimetric method is proposed for the simultaneous analysis of this combination in their pharmaceutical preparation. The method is based upon measurement of the native fluorescence intensity of the two drugs at λ_ex = 233 nm in acetonitrile. The emission data were differentiated using the first (D1) derivative technique. The plots of derivative fluorescence intensity versus concentration were rectilinear over a range of 2–35 and 0.4–8 µg/mL for both ibuprofen (IBU) and famotidine (FAM), respectively. The method was sensitive as the limits of detection were 0.51 and 0.12 µg/mL and limits of quantitation were 1.70 and 0.39 µg/mL, for IBU and FAM respectively. The proposed derivative emission spectrofluorimetric method was successfully applied for the determination of the two drugs in their synthetic mixtures and tablets with good accuracy and precision. The proposed method was validated as per ICH guidelines. Copyright © 2014 John Wiley & Sons, Ltd.     

Prediction,docking study and molecular simulation of 3D DNA aptamers to their targets of endocrine disrupting chemicals

Abstract

Typical endocrine disrupting chemicals, including BPA (Bisphenol A), E₂ (17-β-Estradiol) and PCB 72 (polychlorinated biphenyl 72), are commonly and widely present in the environment with good chemical stability that are difficult to decompose in vitro and in vivo. Most of the high-qualified antibodies are required as the key biomaterials to fabricate the immunosensor for capturing and detecting. As an ideal alternative, the short-chain oligonucleotides (aptamer) are essentially and effectively employed with the advantages of small size, chemical stability and high effectiveness for monitoring these environmental contaminants. However, the molecular interaction, acting site and mode are still not well understood. In this work, we explored the binding features of the aptamers with their targeting ligands. The molecular dynamics simulations were performed on the aptamer–ligand complex systems. The stability of each simulation system was evaluated based on its root-mean-square deviation. The affinities of these proposed ligands and the predicted binding sites are analyzed. According to the binding energy analysis, the affinities between ligands and aptamers and the stability of the systems are BPA?>?PCB 72 >E₂. Trajectory analysis for these three complexes indicated that these three ligands were able to steadily bind with aptamers at docking site from 0 to 50?ns and contributed to alteration of conformation of aptamers.     

Physicochemical studies of processes involving potential photodynamic drugs on route to their targets: self-aggregation and membrane-binding of Zn-hematoporphyrin

The thermodynamics of zinc hematoporphyrin (ZnHP) dimerization and ZnHP-membrane binding were studied. The dimerization equilibrium was determined over the temperature range 19-40 degrees C, using fluorometric techniques. The dimerization constant obtained at 37 degrees C (neutral pH in phosphate-buffered saline) is 4.6 (+/- 0.6) X 10(4) M-1. The dimerization was found to decrease with temperature over the range 19-36 degrees C, the data allowing the extraction of the following thermodynamic parameters for the temperature range 19-31 degrees C: delta G0 = -9.3 kcal/mol, delta H0 = -7.4 kcal/mol, delta S0 = -6.4 eu. For temperatures above 36 degrees C the dimerization was found to be temperature independent, giving the following parameters: delta G0 = -6.6 kcal/mol, delta H0 = 0 kcal/mol, delta S0 = 21.2 eu. On the basis of the data the case is made for the existence of two types of ZnHP dimers, differing in the location of the fifth Zn2+ ligand and in the nature of the contribution of the solvent to the dimerization. For the membrane binding, large unilamellar liposomes served to model biological membranes. The binding of ZnHP to the liposomes was found to be similar, quantitatively, to the corresponding metal-free molecule, namely, fitting a case of one type of site and giving a binding constant of 1600 +/- 160 M (neutral pH and 37 degrees C) which is independent of the length of the porphyrin-liposome.     

Molecular docking of azole drugs and their analogs on CYP121 of Mycobacterium tuberculosis

The Mycobacterium tuberculosis genome codes for 20 different cytochromes. These cytochromes are involved in the breakdown of recalcitrant pollutants and the synthesis of polyketide antibiotics and other complex macromolecules. It has been demonstrated that CYP121 is essential for viability of the bacterium by gene knock-out and complementation studies. CYP121 could therefore be a probable target for the development of new drugs for TB. It has been widely reported that orthologs of CYP121 in fungi are inhibited by azole drugs. We evaluated whether these azole drugs or their structural analogs could bind to and inhibit CYP121 of M. tuberculosis using molecular docking. Six molecules with known anti-CYP121 activity were selected from literature and PubChem database was searched to identify structural analogs for these inhibitors. Three hundred and fifty seven molecules were identified as structural analogs and used in docking studies. Fifty three molecules were found to be scored better than the azole drugs and five of them were ranked among the top 12 molecules by two different scoring functions. These molecules may be further tested by in vitro experimentation for their activity against CYP121 of M. tuberculosis.     

Methods to identify protein targets of metal-based drugs

Metal-based anticancer agents occupy a distinct chemical space due to their particular coordination geometry and reactivity. Despite the initial DNA-targeting paradigm for this class of compounds, it is now clear that they can also be tuned to target proteins in cells, depending on the metal and ligand scaffold. Since metallodrug discovery is dominated by phenotypic screenings, tailored proteomics strategies were crucial to identify and validate protein targets of several investigative and clinically advanced metal-based drugs. Here, such experimental approaches are discussed, which showed that metallodrugs based on ruthenium, gold, rhenium and even platinum, can selectively and specifically target proteins with clear-cut down-stream effects. Target identification strategies are expected to support significantly the mechanism-driven clinical translation of metal-based drugs.     

Repurposing of approved drugs with potential to interact with SARS-CoV-2 receptor

Respiratory transmission is the primary route of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Angiotensin I converting enzyme 2 (ACE2) is the known receptor of SARS-CoV-2 surface spike glycoprotein for entry into human cells. A recent study reported absent to low expression of ACE2 in a variety of human lung epithelial cell samples. Three bioprojects (PRJEB4337, PRJNA270632 and PRJNA280600) invariably found abundant expression of ACE1 (a homolog of ACE2 and also known as ACE) in human lungs compared to very low expression of ACE2. In fact, ACE1 has a wider and more abundant tissue distribution compared to ACE2. Although it is not obvious from the primary sequence alignment of ACE1 and ACE2, comparison of X-ray crystallographic structures show striking similarities in the regions of the peptidase domains (PD) of these proteins, which is known (for ACE2) to interact with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. Critical amino acids in ACE2 that mediate interaction with the viral spike protein are present and organized in the same order in the PD of ACE1. In silico analysis predicts comparable interaction of SARS-CoV-2 spike protein with ACE1 and ACE2. In addition, this study predicts from a list of 1263 already approved drugs that may interact with ACE2 and/or ACE1 and potentially interfere with the entry of SARS-CoV-2 inside the host cells.     

User guide for the discovery of potential drugs via protein structure prediction and ligand docking simulation

Journal of Microbiology - Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In...