The Human Kinome Targeted by FDA Approved Multi-Target Drugs and Combination Products: A Comparative Study from the Drug-Target Interaction Network Perspective

KCNAB2 overexpression inhibits human non-small-cell lung cancer cell growth in vitro and in vivo

Non-small-cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases. NSCLC patients often have poor prognosis demanding urgent identification of novel biomarkers and potential therapeutic targets. KCNAB2 (regulatory beta subunit2 of voltage-gated potassium channel), encoding aldosterone reductase, plays a pivotal role in regulating potassium channel activity. In this research, we tested the expression of KCNAB2 as well as its potential functions in human NSCLC. Bioinformatics analysis shows that expression of KCNAB2 mRNA is significantly downregulated in human NSCLC, correlating with poor overall survival. In addition, decreased KCNAB2 expression was detected in different NSCLC cell lines and local human NSCLC tissues. Exogenous overexpression of KCNAB2 potently suppressed growth, proliferation and motility of established human NSCLC cells and promoted NSCLC cells apoptosis. In contrast, CRISPR/Cas9-induced KCNAB2 knockout further promoted the malignant biological behaviors of NSCLC cells. Protein chip analysis in the KCNAB2-overexpressed NSCLC cells revealed that KCNAB2 plays a possible role in AKT-mTOR cascade activation. Indeed, AKT-mTOR signaling activation was potently inhibited following KCNAB2 overexpression in NSCLC cells. It was however augmented by KCNAB2 knockout. In vivo, the growth of subcutaneous KCNAB2-overexpressed A549 xenografts was significantly inhibited. Collectively, KCNAB2 could be a novel effective gene for prognosis prediction of NSCLC. Targeting KCNAB2 may lead to the development of advanced therapies.

AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors

The discovery of selective and potent kinase inhibitors is crucial for the treatment of various diseases, but the process is challenging due to the high structural similarity among kinases. Efficient kinome-wide bioactivity profiling is essential for understanding kinase function and identifying selective inhibitors. In this study, we propose AiKPro, a deep learning model that combines structure-validated multiple sequence alignments and molecular 3D conformer ensemble descriptors to predict kinase-ligand binding affinities. Our deep learning model uses an attention-based mechanism to capture complex patterns in the interactions between the kinase and the ligand. To assess the performance of AiKPro, we evaluated the impact of descriptors, the predictability for untrained kinases and compounds, and kinase activity profiling based on odd ratios. Our model, AiKPro, shows good Pearson's correlation coefficients of 0.88 and 0.87 for the test set and for the untrained sets of compounds, respectively, which also shows the robustness of the model. AiKPro shows good kinase-activity profiles across the kinome, potentially facilitating the discovery of novel interactions and selective inhibitors. Our approach holds potential implications for the discovery of novel, selective kinase inhibitors and guiding rational drug design.

In silico analysis of a novel protein in CAR T- cell therapy for the treatment of hematologic cancer through molecular modelling, docking, and dynamics approach

Cellular therapy has emerged as a key tool in the treatment of hematological malignancies. An advanced cell therapy known as chimeric antigen receptor T cell therapy (CAR T-cell therapy) has been approved by the United States Food and Drug Administration (FDA) as KYMRIAH by Novartis and YESCARTA by Gilead/Kite pharma in the year 2017. A chimeric receptor is composed of an extracellular antigen recognition site along with some co-stimulating and signalling domains. On the whole, it turns out to be one of the most potent receptors on T cells targeting a specific type of cancer cell based on its antigenic marker. CD19 CAR T-cell therapy is the first clinically approved therapy for lymphoma with remarkable results in complete remission of B cell lymphoblastic leukemia up to 90%. The high rate of effectiveness of the CAR T-cell therapy against B-ALL justifies the investigation and application of this therapy for fatal diseases like all types of hematological malignancies. The most critical aspect of chimeric receptor therapy is designing and building an artificial receptor that is specific to a given type of cancer. For this reason, the in silico technique is an appropriate model to investigate the integrity and effectiveness of the engineered chimeric receptor prior to commencing in vitro experiments followed by clinical trials. This computerized experimental study aids in predicting the molecular mechanism of chimeric protein and how it interacts with both ligands. We have anticipated various features of the chimeric protein in terms of qualitative analysis (structure, protein modelling, physiological properties) and functional analysis (antigenicity, allergenicity, its receptor-ligand binding ability, involving signalling pathways). Furthermore, the reliability and validation of the binding mode of the chimeric protein against receptors were performed through a complex molecular dynamics simulation for a 100 ns timeframe in an aqueous environment. The obtained simulation study showed that CD30 was a better approachable marker as compared to CD20 due to its better binding energy score and also binding conformations stability.

Discovery of Bispecific Lead Compounds from Azadirachta indica against ZIKA NS2B-NS3 Protease and NS5 RNA Dependent RNA Polymerase Using Molecular Simulations

Zika virus (ZIKV) has been characterized as one of many potential pathogens and placed under future epidemic outbreaks by the WHO. However, a lack of potential therapeutics can result in an uncontrolled pandemic as with other human pandemic viruses. Therefore, prioritized effective therapeutics development has been recommended against ZIKV. In this context, the present study adopted a strategy to explore the lead compounds from Azadirachta indica against ZIKV via concurrent inhibition of the NS2B-NS3 protease (ZIKV^pro) and NS5 RNA dependent RNA polymerase (ZIKV^RdRp) proteins using molecular simulations. Initially, structure-based virtual screening of 44 bioflavonoids reported in Azadirachta indica against the crystal structures of targeted ZIKV proteins resulted in the identification of the top four common bioflavonoids, viz. Rutin, Nicotiflorin, Isoquercitrin, and Hyperoside. These compounds showed substantial docking energy (−7.9 to −11.01 kcal/mol) and intermolecular interactions with essential residues of ZIKV^pro (B:His⁵¹, B:Asp⁷⁵, and B:Ser¹³⁵) and ZIKV^RdRp (Asp⁵⁴⁰, Ile⁷⁹⁹, and Asp⁶⁶⁵) by comparison to the reference compounds, O7N inhibitor (ZIKV^pro) and Sofosbuvir inhibitor (ZIKV^RdRp). Besides, long interval molecular dynamics simulation (500 ns) on the selected docked poses reveals stability of the respective docked poses contributed by intermolecular hydrogen bonds and hydrophobic interactions. The predicted complex stability was further supported by calculated end-point binding free energy using molecular mechanics generalized born surface area (MM/GBSA) method. Consequently, the identified common bioflavonoids are recommended as promising therapeutic inhibitors of ZIKV^pro and ZIKV^RdRp against ZIKV for further experimental assessment.

Diabetic Kinome Inhibitors-A New Opportunity for β-Cells Restoration

Diabetes, and several diseases related to diabetes, including cancer, cardiovascular diseases and neurological disorders, represent one of the major ongoing threats to human life, becoming a true pandemic of the 21st century. Current treatment strategies for diabetes mainly involve promoting β-cell differentiation, and one of the most widely studied targets for β-cell regeneration is DYRK1A kinase, a member of the DYRK family. DYRK1A has been characterized as a key regulator of cell growth, differentiation, and signal transduction in various organisms, while further roles and substrates are the subjects of extensive investigation. The targets of interest in this review are implicated in the regulation of β-cells through DYRK1A inhibition-through driving their transition from highly inefficient and death-prone populations into efficient and sufficient precursors of islet regeneration. Increasing evidence for the role of DYRK1A in diabetes progression and β-cell proliferation expands the potential for pharmaceutical applications of DYRK1A inhibitors. The variety of new compounds and binding modes, determined by crystal structure and in vitro studies, may lead to new strategies for diabetes treatment. This review provides recent insights into the initial self-activation of DYRK1A by tyrosine autophosphorylation. Moreover, the importance of developing novel DYRK1A inhibitors and their implications for the treatment of diabetes are thoroughly discussed. The evolving understanding of DYRK kinase structure and function and emerging high-throughput screening technologies have been described. As a final point of this work, we intend to promote the term "diabetic kinome" as part of scientific terminology to emphasize the role of the synergistic action of multiple kinases in governing the molecular processes that underlie this particular group of diseases.

Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs

Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004–17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.

Repurposing existing therapeutics, its importance in oncology drug development: Kinases as a potential target

Repurposing the large arsenal of existing non‐cancer drugs is an attractive proposition to expand the clinical pipelines for cancer therapeutics. The earlier successes in repurposing resulted primarily from serendipitous findings, but more recently, drug or target‐centric systematic identification of repurposing opportunities continues to rise. Kinases are one of the most sought‐after anti‐cancer drug targets over the last three decades. There are many non‐cancer approved drugs that can inhibit kinases as “off‐targets” as well as many existing kinase inhibitors that can target new additional kinases in cancer. Identifying cancer‐associated kinase inhibitors through mining commercial drug databases or new kinase targets for existing inhibitors through comprehensive kinome profiling can offer more effective trial‐ready options to rapidly advance drugs for clinical validation. In this review, we argue that drug repurposing is an important approach in modern drug development for cancer therapeutics. We have summarized the advantages of repurposing, the rationale behind this approach together with key barriers and opportunities in cancer drug development. We have also included examples of non‐cancer drugs that inhibit kinases or are associated with kinase signalling as a basis for their anti‐cancer action.

Understanding the Polypharmacological Profiles of Triple Reuptake Inhibitors by Molecular Simulation

The triple reuptake inhibitors (TRIs) class is a class of effective inhibitors of human monoamine transporters (hMATs), which includes dopamine, norepinephrine, and serotonin transporters (hDATs, hNETs, and hSERTs). Due to the high degree of structural homology of the binding sites of those transporters, it is a great challenge to design potent TRIs with fine-tuned binding profiles. The molecular determinants responsible for the binding selectivity of TRIs to hDATs, hNETs, and hSERTs remain elusive. In this study, the solved X-ray crystallographic structure of hSERT in complex with escitalopram was used as a basis for modeling nine complexes of three representative TRIs (SEP225289, NS2359, and EB1020) bound to their corresponding targets. Molecular dynamics (MD) and effective post-trajectory analysis were performed to estimate the drug binding free energies and characterize the selective profiles of each TRI to hMATs. The common binding mode of studied TRIs to hMATs was revealed by hierarchical clustering analysis of the per-residue energy. Furthermore, the combined protein-ligand interaction fingerprint and residue energy contribution analysis indicated that several conserved and nonconserved "Warm Spots" such as S149, V328, and M427 in hDAT, F317, F323, and V325 in hNET and F335, F341, and V343 in hSERT were responsible for the TRI-binding selectivity. These findings provided important information for rational design of a single drug with better polypharmacological profiles through modulating multiple targets.

Druggable genome and precision medicine in cancer: current challenges

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Multitargeting application of proline-derived peptidomimetics addressing cancer-related human matrix metalloproteinase 9 and carbonic anhydrase II

A series of d-proline peptidomimetics were evaluated as dual inhibitors of both human carbonic anhydrases (hCAs) and human gelatinases (MMP2 and MMP9), as these enzymes are both involved in the carcinogenesis and tumor invasion processes. The synthesis and enzyme inhibition kinetics of d-proline derivatives containing a biphenyl sulfonamido moiety revealed an interesting inhibition profile of compound XIV towards MMP9 and CAII. The SAR analysis and docking studies revealed a stringent requirement of a trans geometry for the two arylsulfonyl moieties, which are both necessary for inhibition of MMP9 and CAII. As MMP9 and CAII enzymes are both overexpressed in gastrointestinal stromal tumor cells, this molecule may represent an interesting chemical probe for a multitargeting approach on gastric and colorectal cancer.

Relevant Features of Polypharmacologic Human-Target Antimicrobials Discovered by Machine-Learning Techniques

Polypharmacologic human-targeted antimicrobials (polyHAM) are potentially useful in the treatment of complex human diseases where the microbiome is important (e.g., diabetes, hypertension). We previously reported a machine-learning approach to identify polyHAM from FDA-approved human targeted drugs using a heterologous approach (training with peptides and non-peptide compounds). Here we discover that polyHAM are more likely to be found among antimicrobials displaying a broad-spectrum antibiotic activity and that topological, but not chemical features, are most informative to classify this activity. A heterologous machine-learning approach was trained with broad-spectrum antimicrobials and tested with human metabolites; these metabolites were labeled as antimicrobials or non-antimicrobials based on a naïve text-mining approach. Human metabolites are not commonly recognized as antimicrobials yet circulate in the human body where microbes are found and our heterologous model was able to classify those with antimicrobial activity. These results provide the basis to develop applications aimed to design human diets that purposely alter metabolic compounds proportions as a way to control human microbiome.

Recent Advances and Challenges of the Drugs Acting on Monoamine Transporters

Background:

The human Monoamine Transporters (hMATs), primarily including hSERT, hNET and hDAT, are important targets for the treatment of depression and other behavioral disorders with more than the availability of 30 approved drugs.

Objective:

This paper is to review the recent progress in the binding mode and inhibitory mechanism of hMATs inhibitors with the central or allosteric binding sites, for the benefit of future hMATs inhibitor design and discovery. The Structure-Activity Relationship (SAR) and the selectivity for hit/lead compounds to hMATs that are evaluated by in vitro and in vivo experiments will be highlighted.

Methods:

PubMed and Web of Science databases were searched for protein-ligand interaction, novel inhibitors design and synthesis studies related to hMATs.

Results:

Literature data indicate that since the first crystal structure determinations of the homologous bacterial Leucine Transporter (LeuT) complexed with clomipramine, a sizable database of over 100 experimental structures or computational models has been accumulated that now defines a substantial degree of structural variability hMATs-ligands recognition. In the meanwhile, a number of novel hMATs inhibitors have been discovered by medicinal chemistry with significant help from computational models.

Conclusion:

The reported new compounds act on hMATs as well as the structures of the transporters complexed with diverse ligands by either experiment or computational modeling have shed light on the poly-pharmacology, multimodal and allosteric regulation of the drugs to transporters. All of the studies will greatly promote the Structure-Based Drug Design (SBDD) of structurally novel scaffolds with high activity and selectivity for hMATs.

Synergistic Anti Leukemia Effect of a Novel Hsp90 and a Pan Cyclin Dependent Kinase Inhibitors

Acute myeloid leukemia (AML) is among the top four malignancies in Saudi nationals, and it is the top leukemia subtype worldwide. Resistance to available AML drugs requires the identification of new targets and agents. Hsp90 is one of the emerging important targets in AML, which has a central role in the regulation of apoptosis and cell proliferation through client proteins including the growth factor receptors and cyclin dependent kinases. The objective of the first part of this study is to investigate the putative Hsp90 inhibition activity of three novel previously synthesized quinazolines, which showed HL60 cytotoxicity and VEGFR2 and EGFR kinases inhibition activities. Using surface plasmon resonance, compound 1 (HAA₂₀₂₀) showed better Hsp90 inhibition compared to 17-AAG, and a docking study revealed that it fits nicely into the ATPase site. The objective of the second part is to maximize the anti-leukemic activity of HAA₂₀₂₀, which was combined with each of the eleven standard inhibitors. The best resulting synergistic effect in HL60 cells was with the pan cyclin-dependent kinases (CDK) inhibitor dinaciclib, using an MTT assay. Furthermore, the inhibiting effect of the Hsp90α gene by the combination of HAA₂₀₂₀ and dinaciclib was associated with increased caspase-7 and TNF-α, leading to apoptosis in HL60 cells. In addition, the combination upregulated p27 simultaneously with the inhibition of cyclinD3 and CDK2, leading to abolished HL60 proliferation and survival. The actions of HAA₂₀₂₀ propagated the apoptotic and cell cycle control properties of dinaciclib, showing the importance of co-targeting Hsp90 and CDK, which could lead to the better management of leukemia.

A Definition of "Multitargeticity": Identifying Potential Multitarget and Selective Ligands Through a Vector Analysis

The design of multitarget drugs is an essential area of research in Medicinal Chemistry since they have been proposed as potential therapeutics for the management of complex diseases. However, defining a multitarget drug is not an easy task. In this work, we propose a vector analysis for measuring and defining "multitargeticity." We developed terms, such as order and force of a ligand, to finally reach two parameters: multitarget indexes 1 and 2. The combination of these two indexes allows discrimination of multitarget drugs. Several training sets were constructed to test the usefulness of the indexes: an experimental training set, with real affinities, a docking training set, within theoretical values, and an extensive database training set. The indexes proved to be useful, as they were used independently in silico and experimental data, identifying actual multitarget compounds and even selective ligands in most of the training sets. We then applied these indexes to evaluate a virtual library of potential ligands for targets related to multiple sclerosis, identifying 10 compounds that are likely leads for the development of multitarget drugs based on their in silico behavior. With this work, a new milestone is made in the way of defining multitargeticity and in drug design.

A novel drug–drug coamorphous system without molecular interactions: improve the physicochemical properties of tadalafil and repaglinide

Tadalafil and repaglinide, categorized as BCS class II drugs, have low oral bioavailabilities due to their poorly aqueous solubilities and dissolutions. The aim of this study was to enhance the dissolution of tadalafil and repaglinide by co-amorphization technology and evaluate the storage and compression stability of such coamorphous system. Based on Flory–Huggins interaction parameter (χ ≤ 0) and Hansen solubility parameter (δ_t ≤ 7 MPa^0.5) calculations, tadalafil and repaglinide was predicted to be well miscible with each other. Coamorphous tadalafil–repaglinide (molar ratio, 1 : 1) was prepared by solvent-evaporation method and characterized with respect to its thermal properties, possible molecular interactions. A single T_g (73.1 °C) observed in DSC and disappearance of crystallinity in PXRD indicated the formation of coamorphous system. Principal component analysis of FTIR in combination with Raman spectroscopy and Ss ¹³C NMR suggested the absence of intermolecular interactions in coamorphous tadalafil–repaglinide. In comparison to pure crystalline forms and their physical mixtures, both drugs in coamorphous system exhibited significant increases in intrinsic dissolution rate (1.5–3-fold) and could maintain supersaturated level for at least 4 hours in non-sink dissolution. In addition, the coamorphous tadalafil–repaglinide showed improved stability compared to the pure amorphous forms under long-term stability and accelerated storage conditions as well as under high compressing pressure. In conclusion, this study showed that co-amorphization technique is a promising approach for improving the dissolution rate of poorly water-soluble drugs and for stabilizing amorphous drugs.

The coamorphous tadalafil–repaglinide (molar ratio, 1 : 1) prepared by solvent-evaporation method significantly improve the physicochemical properties of tadalafil and repaglinide.

Dual Inhibitors as a New Challenge for Cancer Multidrug Resistance Treatment

BACKGROUND

Dual-targeting in cancer treatment by a single drug is an unconventional approach in relation to drug combinations. The rationale for the development of dualtargeting agents is to overcome incomplete efficacy and drug resistance frequently present when applying individual targeting agents. Consequently, -a more favorable outcome of cancer treatment is expected with dual-targeting strategies.

METHODS

We reviewed the literature, concentrating on the association between clinically relevant and/or novel dual inhibitors with the potential to modulate multidrug resistant phenotype of cancer cells, particularly the activity of P-glycoprotein. A balanced analysis of content was performed to emphasize the most important findings and optimize the structure of this review.

RESULTS

Two-hundred and forty-five papers were included in the review. The introductory part was interpreted by 9 papers. Tyrosine kinase inhibitors' role in the inhibition of Pglycoprotein and chemosensitization was illustrated by 87 papers. The contribution of naturalbased compounds in overcoming multidrug resistance was reviewed using 92 papers, while specific dual inhibitors acting against microtubule assembling and/or topoisomerases were described with 55 papers. Eleven papers gave an insight into a novel and less explored approach with hybrid drugs. Their influence on P-glycoprotein and multidrug resistance was also evaluated.

CONCLUSION

These findings bring into focus rational anticancer strategies with dual-targeting agents. Most evaluated synthetic and natural drugs showed a great potential in chemosensitization. Further steps in this direction are needed for the optimization of anticancer treatment.

Recent Technological Advances in the Mass Spectrometry-based Nanomedicine Studies: An Insight from Nanoproteomics

Nanoscience becomes one of the most cutting-edge research directions in recent years since it is gradually matured from basic to applied science. Nanoparticles (NPs) and nanomaterials (NMs) play important roles in various aspects of biomedicine science, and their influences on the environment have caused a whole range of uncertainties which require extensive attention. Due to the quantitative and dynamic information provided for human proteome, mass spectrometry (MS)-based quantitative proteomic technique has been a powerful tool for nanomedicine study. In this article, recent trends of progress and development in the nanomedicine of proteomics were discussed from quantification techniques and publicly available resources or tools. First, a variety of popular protein quantification techniques including labeling and label-free strategies applied to nanomedicine studies are overviewed and systematically discussed. Then, numerous protein profiling tools for data processing and postbiological statistical analysis and publicly available data repositories for providing enrichment MS raw data information sources are also discussed.

Prediction of the binding mode and resistance profile for a dual-target pyrrolyl diketo acid scaffold against HIV-1 integrase and reverse-transcriptase-associated ribonuclease H

The rapid emergence of drug-resistant variants is one of the most common causes of highly active antiretroviral therapeutic (HAART) failure in patients infected with HIV-1. Compared with the existing HAART, the recently developed pyrrolyl diketo acid scaffold targeting both HIV-1 integrase (IN) and reverse transcriptase-associated ribonuclease H (RNase H) is an efficient approach to counteract the failure of anti-HIV treatment due to drug resistance. However, the binding mode and potential resistance profile of these inhibitors with important mechanistic principles remain poorly understood. To address this issue, an integrated computational method was employed to investigate the binding mode of inhibitor JMC6F with HIV-1 IN and RNase H. By using per-residue binding free energy decomposition analysis, the following residues: Asp64, Thr66, Leu68, Asp116, Tyr143, Gln148 and Glu152 in IN, Asp443, Glu478, Trp536, Lys541 and Asp549 in RNase H were identified as key residues for JMC6F binding. And then computational alanine scanning was carried to further verify the key residues. Moreover, the resistance profile of the currently known major mutations in HIV-1 IN and 2 mutations in RNase H against JMC6F was predicted by in silico mutagenesis studies. The results demonstrated that only three mutations in HIV-1 IN (Y143C, Q148R and N155H) and two mutations in HIV-1 RNase H (Y501R and Y501W) resulted in a reduction of JMC6F potency, thus indicating their potential role in providing resistance to JMC6F. These data provided important insights into the binding mode and resistance profile of the inhibitors with a pyrrolyl diketo acid scaffold in HIV-1 IN and RNase H, which would be helpful for the development of more effective dual HIV-1 IN and RNase H inhibitors.

Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein⁻protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.

What Makes Species Productive of Anti-Cancer Drugs? Clues from Drugs' Species Origin, Druglikeness, Target and Pathway

BACKGROUND

Despite the substantial contribution of natural products to the FDA drug approval list, the discovery of anti-cancer drugs from the huge amount of species on the planet remains looking for a needle in a haystack.

OBJECTIVE

Drug-productive clusters in the phylogenetic tree are thus proposed to narrow the searching scope by focusing on much smaller amount of species within each cluster, which enable prioritized and rational bioprospecting for novel drug-like scaffolds. However, the way anti-cancer nature-derived drugs distribute in phylogenetic tree has not been reported, and it is oversimplified to just focus anti-cancer drug discovery on the drug-productive clusters, since the number of species in each cluster remains too large to be managed.

METHODS

In this study, 260 anti-cancer drugs approved in the past 70 years were comprehensively analyzed by hierarchical clustering of phylogenetic distribution.

RESULTS

207 out of these 260 drugs were derived from or inspired by the natural products isolated from 58 species. Phylogenetic distribution of those drugs further revealed that nature-derived anti-cancer drugs originated mostly from drug-productive families that tend to be clustered rather than scattered on the phylogenetic tree. Moreover, based on their productivity, drug-producing species were categorized into productive (CPS), newly emerging (CNS) and lessproductive (CLS). Statistical significances in druglikeness between drugs from CPS and CLS were observed, and drugs from CNS were found to share similar drug-like properties to those from CPS.

CONCLUSION

This finding indicated a great raise in drug approval standard, which suggested us to focus bioprospecting on the species yielding multiple drugs and keeping productive for long period of time.

High-throughput binding affinity calculations at extreme scales

Background

Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.

Results

We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.

Conclusions

As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

Prediction of GluN2B-CT1290-1310/DAPK1 Interaction by Protein⁻Peptide Docking and Molecular Dynamics Simulation

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290⁻1310 of GluN2B (GluN2B-CT_1290-1310) remain elusive and the interaction between GluN2B-CT_1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT_1290-1310 was predicted by computational simulation methods including protein⁻peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT_1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT_1290⁻1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT_1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein⁻peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT_1290-1310 and DAPK1 interface.

Determining the Balance Between Drug Efficacy and Safety by the Network and Biological System Profile of Its Therapeutic Target

One of the most challenging puzzles in drug discovery is the identification and characterization of candidate drug of well-balanced profile between efficacy and safety. So far, extensive efforts have been made to evaluate this balance by estimating the quantitative structure–therapeutic relationship and exploring target profile of adverse drug reaction. Particularly, the therapeutic index (TI) has emerged as a key indicator illustrating this delicate balance, and a clinically successful agent requires a sufficient TI suitable for it corresponding indication. However, the TI information are largely unknown for most drugs, and the mechanism underlying the drugs with narrow TI (NTI drugs) is still elusive. In this study, the collective effects of human protein–protein interaction (PPI) network and biological system profile on the drugs' efficacy–safety balance were systematically evaluated. First, a comprehensive literature review of the FDA approved drugs confirmed their NTI status. Second, a popular feature selection algorithm based on artificial intelligence (AI) was adopted to identify key factors differencing the target mechanism between NTI and non-NTI drugs. Finally, this work revealed that the targets of NTI drugs were highly centralized and connected in human PPI network, and the number of similarity proteins and affiliated signaling pathways of the corresponding targets was much higher than those of non-NTI drugs. These findings together with the newly discovered features or feature groups clarified the key factors indicating drug's narrow TI, and could thus provide a novel direction for determining the delicate drug efficacy-safety balance.

Coral: Clear and Customizable Visualization of Human Kinome Data

Protein kinases represent one of the largest gene families in eukaryotes and play roles in a wide range of cell signaling processes and human diseases. Current tools for visualizing kinase data in the context of the human kinome superfamily are limited to encoding data through the addition of nodes to a low-resolution image of the kinome tree. We present Coral, a user-friendly interactive web application for visualizing both quantitative and qualitative data. Unlike previous tools, Coral can encode data in three features (node color, node size, and branch color), allows three modes of kinome visualization (the traditional kinome tree as well as radial and dynamic force networks), and generates high-resolution scalable vector graphics files suitable for publication without the need for refinement using graphics editing software. Due to its user-friendly, interactive, and highly customizable design, Coral is broadly applicable to high-throughput studies of the human kinome. The source code and web application are available at github.com/dphansti/CORAL and phanstiel-lab.med.unc.edu/Coral, respectively.

Identification of novel immune-relevant drug target genes for Alzheimer's Disease by combining ontology inference with network analysis

AIMS

Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune-relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties.

METHODS

A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well-known pathway-related databases were conducted to explore the function of the identified potential drug target genes.

RESULTS

Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that the enriched terms were primarily focus on immune-relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting.

CONCLUSION

The well-reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification

Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.

Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations

Metabotropic glutamate receptor 5 (mGlu₅) plays a key role in synaptic information storage and memory, which is a well-known target for a variety of psychiatric and neurodegenerative disorders. In recent years, the increasing efforts have been focused on the design of allosteric modulators, and the negative allosteric modulators (NAMs) are the front-runners. Recently, the architecture of the transmembrane (TM) domain of mGlu₅ receptor has been determined by crystallographic experiment. However, it has been not well understood how the pharmacophores of NAMs accommodated into the allosteric binding site. In this study, molecular dynamics (MD) simulations were performed on mGlu₅ receptor bound with NAMs in preclinical or clinical development to shed light on this issue. In order to identify the key residues, the binding free energies as well as per-residue contributions for NAMs binding to mGlu₅ receptor were calculated. Subsequently, the in silico site-directed mutagenesis of the key residues was performed to verify the accuracy of simulation models. As a result, the shared common features of the studied 5 clinically important NAMs (mavoglurant, dipraglurant, basimglurant, STX107, and fenobam) interacting with 11 residues in allosteric site were obtained. This comprehensive study presented a better understanding of mGlu₅ receptor NAMs binding mechanism, which would be further used as a useful framework to assess and discover novel lead scaffolds for NAMs.

What Contributes to Serotonin-Norepinephrine Reuptake Inhibitors' Dual-Targeting Mechanism? The Key Role of Transmembrane Domain 6 in Human Serotonin and Norepinephrine Transporters Revealed by Molecular Dynamics Simulation

Dual inhibition of serotonin and norepinephrine transporters (hSERT and hNET) gives greatly improved efficacy and tolerability for treating major depressive disorder (MDD) compared with selective reuptake inhibitors. Pioneer studies provided valuable information on structure, function, and pharmacology of drugs targeting both hSERT and hNET (serotonin-norepinephrine reuptake inhibitors, SNRIs), and the differential binding mechanism between SNRIs and selective inhibitors of 5-HT (SSRIs) or NE (sNRIs) to their corresponding targets was expected to be able to facilitate the discovery of a privileged drug-like scaffold with improved efficacy. However, the dual-target mechanism of SNRIs was still elusive, and the binding mode distinguishing SNRIs from SSRIs and sNRIs was also unclear. Herein, an integrated computational strategy was adopted to discover the binding mode shared by all FDA approved SNRIs. The comparative analysis of binding free energy at the per-residue level discovered that residues Phe335, Leu337, Gly338, and Val343 located at the transmembrane domain 6 (TM6) of hSERT (the corresponding residues Phe317, Leu319, Gly320, and Val325 in hNET) were the determinants accounting for SNRIs' dual-acting inhibition, while residues lining TM3 and 8 (Ile172, Ser438, Thr439, and Leu443 in hSERT; Val148, Ser419, Ser420, and Met424 in hNET) contributed less to the binding of SNRIs than that of SSRIs and sNRIs. Based on these results, the distances between an SNRI's centroid and the centroids of its two aromatic rings (measuring the depth of rings stretching into hydrophobic pockets) were discovered as the key to the SNRIs' dual-targeting mechanism. This finding revealed SNRIs' binding mechanism at an atomistic level, which could be further utilized as structural blueprints for the rational design of privileged drug-like scaffolds treating MDD.

Revealing vilazodone's binding mechanism underlying its partial agonism to the 5-HT1A receptor in the treatment of major depressive disorder

It has been estimated that major depressive disorder (MDD) will become the second largest global burden among all diseases by 2030. Various types of drugs, including selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and serotonin receptor partial agonist/reuptake inhibitors (SPARIs), have been approved and become the primary or first-line medications prescribed for MDD. SPARI was expected to demonstrate more enhanced drug efficacy and a rapid onset of action as compared to SSRI and SNRI. As one of the most famous SPARIs, vilazodone was approved by the FDA for the treatment of MDD. Because of the great clinical importance of vilazodone, its binding mechanism underlying its partial agonism to the 5-HT_1A receptor (5-HT_1AR) could provide valuable information to SPARIs' drug-like properties. However, this mechanism has not been reported to date; consequently, the rational design of new efficacious SPARI-based MDD drugs is severely hampered. To explore the molecular mechanism of vilazodone, an integrated computational strategy was adopted in this study to reveal its binding mechanism and prospective structural feature at the agonist binding site of 5-HT_1AR. As a result, 22 residues of this receptor were identified as hotspots, consistently favoring the binding of vilazodone and its analogues, and a common binding mechanism underlying their partial agonism to 5-HT_1AR was, therefore, discovered. Moreover, three main interaction features between vilazodone and 5-HT_1AR have been revealed and schematically summarized. In summary, this newly identified binding mechanism will provide valuable information for medicinal chemists working in the field of rational design of novel SPARIs for MDD treatment.

A perspective on multi-target drug discovery and design for complex diseases

Diseases of infection, of neurodegeneration (such as Alzheimer’s and Parkinson’s diseases), and of malignancy (cancers) have complex and varied causative factors. Modern drug discovery has the power to identify potential modulators for multiple targets from millions of compounds. Computational approaches allow the determination of the association of each compound with its target before chemical synthesis and biological testing is done. These approaches depend on the prior identification of clinically and biologically validated targets. This Perspective will focus on the molecular and computational approaches that underpin drug design by medicinal chemists to promote understanding and collaboration with clinical scientists.

Assessing the Performances of Protein Function Prediction Algorithms from the Perspectives of Identification Accuracy and False Discovery Rate

The function of a protein is of great interest in the cutting-edge research of biological mechanisms, disease development and drug/target discovery. Besides experimental explorations, a variety of computational methods have been designed to predict protein function. Among these in silico methods, the prediction of BLAST is based on protein sequence similarity, while that of machine learning is also based on the sequence, but without the consideration of their similarity. This unique characteristic of machine learning makes it a good complement to BLAST and many other approaches in predicting the function of remotely relevant proteins and the homologous proteins of distinct function. However, the identification accuracies of these in silico methods and their false discovery rate have not yet been assessed so far, which greatly limits the usage of these algorithms. Herein, a comprehensive comparison of the performances among four popular prediction algorithms (BLAST, SVM, PNN and KNN) was conducted. In particular, the performance of these methods was systematically assessed by four standard statistical indexes based on the independent test datasets of 93 functional protein families defined by UniProtKB keywords. Moreover, the false discovery rates of these algorithms were evaluated by scanning the genomes of four representative model organisms (Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Mycobacterium tuberculosis). As a result, the substantially higher sensitivity of SVM and BLAST was observed compared with that of PNN and KNN. However, the machine learning algorithms (PNN, KNN and SVM) were found capable of substantially reducing the false discovery rate (SVM < PNN < KNN). In sum, this study comprehensively assessed the performance of four popular algorithms applied to protein function prediction, which could facilitate the selection of the most appropriate method in the related biomedical research.

Computational identification of the binding mechanism of a triple reuptake inhibitor amitifadine for the treatment of major depressive disorder

A shared binding mode involving eleven key residues at the S1 site of MATs for the binding of amitifadine is identified.

Computational characterization of the selective inhibition of human norepinephrine and serotonin transporters by an escitalopram scaffold

Selective inhibition of human norepinephrine and serotonin transporters has been studied by computational approaches. 4 warm spots in hNET and 4 in hSERT were found to exert a pronounced effect on inhibition by the studied ligands.

Differentiating Physicochemical Properties between Addictive and Nonaddictive ADHD Drugs Revealed by Molecular Dynamics Simulation Studies

Attention-deficit/hyperactivity disorder (ADHD) is the most commonly diagnosed mental disorder of children and adolescents. Although psychostimulants are currently the first-line drugs for ADHD, their highly addictive profile raises great abuse concerns. It is known that psychostimulants' addictiveness is largely attributed to their interaction with dopamine transporter (DAT) and their binding modes in DAT can thus facilitate the understanding of the mechanism underlining drugs' addictiveness. However, no DAT residue able to discriminate ADHD drugs' addictiveness is identified, and the way how different drug structures affect their abuse liability is still elusive. In this study, multiple computational methods were integrated to differentiate binding modes between approved psychostimulants and ADHD drugs of little addictiveness. As a result, variation in energy contribution of 8 residues between addictive and nonaddictive drugs was observed, and a reduction in hydrophobicity of drugs' 2 functional groups was identified as the indicator of drugs' addictiveness. This finding agreed well with the physicochemical properties of 8 officially reported controlled substances. The identified variations in binding mode can shed light on the mechanism underlining drugs' addictiveness, which may thus facilitate the discovery of improved ADHD therapeutics with reduced addictive profile.

Chemistry-based molecular signature underlying the atypia of clozapine

The central nervous system is functionally organized as a dynamic network of interacting neural circuits that underlies observable behaviors. At higher resolution, these behaviors, or phenotypes, are defined by the activity of a specific set of biomolecules within those circuits. Identification of molecules that govern psychiatric phenotypes is a major challenge. The only organic molecular entities objectively associated with psychiatric phenotypes in humans are drugs that induce psychiatric phenotypes and drugs used for treatment of specific psychiatric conditions. Here, we identified candidate biomolecules contributing to the organic basis for psychosis by deriving an in vivo biomolecule-tissue signature for the atypical pharmacologic action of the antipsychotic drug clozapine. Our novel in silico approach identifies the ensemble of potential drug targets based on the drug's chemical structure and the region-specific gene expression profile of each target in the central nervous system. We subtracted the signature of the action of clozapine from that of a typical antipsychotic, chlorpromazine. Our results implicate dopamine D4 receptors in the pineal gland and muscarinic acetylcholine M1 (CHRM1) and M3 (CHRM3) receptors in the prefrontal cortex (PFC) as significant and unique to clozapine, whereas serotonin receptors 5-HT_2A in the PFC and 5-HT_2C in the caudate nucleus were common significant sites of action for both drugs. Our results suggest that D4 and CHRM1 receptor activity in specific tissues may represent underappreciated drug targets to advance the pharmacologic treatment of schizophrenia. These findings may enhance our understanding of the organic basis of psychiatric disorders and help developing effective therapies.