Targeting cytotoxic lymphocyte antigen 4 (CTLA-4) in breast cancer.

Breast cancer (BC) has a high mortality rate and is one of the most common malignancies in the world. Initially, BC was considered non-immunogenic, but a paradigm shift occurred with the discovery of tumor-infiltrating lymphocytes (TILs) and regulatory T cells (Tregs) in the BC tumor microenvironment. CTLA-4 (Cytotoxic T-lymphocyte-associated protein 4) immunotherapy has emerged as a treatment option for BC, but it has limitations, including suboptimal antitumor effects and toxicity. Research has demonstrated that anti-CTLA-4 combination therapies, such as Treg depletion, cancer vaccines, and modulation of the gut microbiome, are significantly more effective than CTLA-4 monoclonal antibody (mAB) monotherapy. Second-generation CTLA-4 antibodies are currently being developed to mitigate immune-related adverse events (irAEs) and augment antitumor efficacy. This review examines anti-CTLA-4 mAB in BC, both as monotherapy and in combination with other treatments, and sheds light on ongoing clinical trials, novel CTLA-4 therapeutic strategies, and potential utility of biomarkers in BC.

Structural analysis of hERG channel blockers and the implications for drug design.

The repolarizing current (Ikr) produced by the hERG potassium channel forms a major component of the cardiac action potential and blocking this current by small molecule drugs can lead to life-threatening cardiotoxicity. Understanding the mechanisms of drug-mediated hERG inhibition is essential to develop a second generation of safe drugs, with minimal cardiotoxic effects. Although various computational tools and drug design guidelines have been developed to avoid binding of drugs to the hERG pore domain, there are many other aspects that are still open for investigation. This includes the use computational modelling to study the implications of hERG mutations on hERG structure and trafficking, the interactions of hERG with hERG chaperone proteins and with membrane-soluble molecules, the mechanisms of drugs that inhibit hERG trafficking and drugs that rescue hERG mutations. The plethora of available experimental data regarding all these aspects can guide the construction of much needed robust computational structural models to study these mechanisms for the rational design of safe drugs.

Discovery of allosteric SHP2 inhibitors through ensemble-based consensus molecular docking, endpoint and absolute binding free energy calculations.

SHP2 (Src homology-2 domain-containing protein tyrosine phosphatase-2) is a cytoplasmic protein -tyrosine phosphatase encoded by the gene PTPN11. It plays a crucial role in regulating cell growth and differentiation. Specifically, SHP2 is an oncoprotein associated with developmental pathologies and several different cancer types, including gastric, leukemia and breast cancer and is of great therapeutic interest. Given these roles, current research efforts have focused on developing SHP2 inhibitors. Allosteric SHP2 inhibitors have been shown to be more selective and pharmacologically appealing compared to competitive catalytic inhibitors targeting SHP2. Nevertheless, there remains a need for novel allosteric inhibitor scaffolds targeting SHP2 to develop compounds with improved selectivity, cell permeability, and bioavailability. Towards this goal, this study applied various computational tools to screen over 6 million compounds against the allosteric site within SHP2. The top-ranked hits from our in-silico screening were validated using protein thermal shift and biolayer interferometry assays, revealing three potent compounds. Kinetic binding assays were employed to measure the binding affinities of the top-ranked compounds and demonstrated that they all bind to SHP2 with a nanomolar affinity. Hence the compounds and the computational workflow described herein provide an effective approach for identifying and designing a generation of improved allosteric inhibitors of SHP2.

Modulation of ERCC1-XPF Heterodimerization Inhibition via Structural Modification of Small Molecule Inhibitor Side-Chains.

Inhibition of DNA repair enzymes is an attractive target for increasing the efficacy of DNA damaging chemotherapies. The ERCC1-XPF heterodimer is a key endonuclease in numerous single and double strand break repair processes, and inhibition of the heterodimerization has previously been shown to sensitize cancer cells to DNA damage. In this work, the previously reported ERCC1-XPF inhibitor 4 was used as the starting point for an in silico study of further modifications of the piperazine side-chain. A selection of the best scoring hits from the in silico screen were synthesized using a late stage functionalization strategy which should allow for further iterations of this class of inhibitors to be readily synthesized. Of the synthesized compounds, compound 6 performed the best in the in vitro fluorescence based endonuclease assay. The success of compound 6 in inhibiting ERCC1-XPF endonuclease activity in vitro translated well to cell-based assays investigating the inhibition of nucleotide excision repair and disruption of heterodimerization. Subsequently compound 6 was shown to sensitize HCT-116 cancer cells to treatment with UVC, cyclophosphamide, and ionizing radiation. This work serves as an important step towards the synergistic use of DNA repair inhibitors with chemotherapeutic drugs.

Tackling Immune Targets for Breast Cancer: Beyond PD-1/PD-L1 Axis.

The burden of breast cancer is imposing a huge global problem. Drug discovery research and novel approaches to treat breast cancer have been carried out extensively over the last decades. Although immune checkpoint inhibitors are showing promising preclinical and clinical results in treating breast cancer, they are facing multiple limitations. From an immunological perspective, a recent report highlighted breast cancer as an "inflamed tumor" with an immunosuppressive microenvironment. Consequently, researchers have been focusing on identifying novel immunological targets that can tune up the tumor immune microenvironment. In this context, several novel non-classical immune targets have been targeted to determine their ability to uncouple immunoregulatory pathways at play in the tumor microenvironment. This article will highlight strategies designed to increase the immunogenicity of the breast tumor microenvironment. It also addresses the latest studies on targets which can enhance immune responses to breast cancer and discusses examples of preclinical and clinical trial landscapes that utilize these targets.

A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.

Off-target interactions of drugs with the human ether-à-go-go related gene 1 (hERG1) channel have been associated with severe cardiotoxic conditions leading to the withdrawal of many drugs from the market over the last decades. Consequently, predicting drug-induced hERG-liability is now a prerequisite in any drug discovery campaign. Understanding the atomic level interactions of drug with the channel is essential to guide the efficient development of safe drugs. Here we utilize the recent cryo-EM structure of the hERG channel and describe an integrated computational workflow to characterize different drug-hERG interactions. The workflow employs various structure-based approaches and provides qualitative and quantitative insights into drug binding to hERG. Our protocol accurately differentiated the strong blockers from weak and revealed three potential anchoring sites in hERG. Drugs engaging in all these sites tend to have high affinity towards hERG. Our results were cross-validated using a fluorescence polarization kit binding assay and with electrophysiology measurements on the wild-type (WT-hERG) and on the two hERG mutants (Y652A-hERG and F656A-hERG), using the patch clamp technique on HEK293 cells. Finally, our analyses show that drugs binding to hERG disrupt and hijack certain native-structural networks in the channel, thereby, gaining more affinity towards hERG.

Computer-aided drug design of small molecule inhibitors of the ERCC1-XPF protein-protein interaction.

The heterodimer of DNA excision repair protein ERCC-1 and DNA repair endonuclease XPF (ERCC1-XPF) is a 5'-3' structure-specific endonuclease essential for the nucleotide excision repair (NER) pathway, and it is also involved in other DNA repair pathways. In cancer cells, ERCC1-XPF plays a central role in repairing DNA damage induced by chemotherapeutics including platinum-based and cross-linking agents; thus, its inhibition is a promising strategy to enhance the effect of these therapies. In this study, we rationally modified the structure of F06, a small molecule inhibitor of the ERCC1-XPF interaction (Molecular Pharmacology, 84, 2013 and 12), to improve its binding to the target. We followed a multi-step computational approach to investigate potential modification sites of F06, rationally design and rank a library of analogues, and identify candidates for chemical synthesis and in vitro testing. Our top compound, B5, showed an improved half-maximum inhibitory concentration (IC50 ) value of 0.49 µM for the inhibition of ERCC1-XPF endonuclease activit, and lays the foundation for further testing and optimization. Also, the computational approach reported here can be used to develop DNA repair inhibitors targeting the ERCC1-XPF complex.

Targeting DNA Repair in Tumor Cells via Inhibition of ERCC1-XPF.

The ERCC1-XPF heterodimer is a 5'-3' structure-specific endonuclease, which plays an essential role in several DNA repair pathways in mammalian cells. ERCC1-XPF is primarily involved in the repair of chemically induced helix-distorting and bulky DNA lesions, such as cyclobutane pyrimidine dimers (CPDs), and DNA interstrand cross-links. Inhibition of ERCC1-XPF has been shown to potentiate cytotoxicity of platinum-based drugs and cyclophosphamide in cancer cells. In this study, the previously described ERCC1-XPF inhibitor 4-((6-chloro-2-methoxyacridin-9-yl)amino)-2-((4-methylpiperazin-1-yl)methyl)phenol (compound 1) was used as a reference compound. Following the outcome of docking-based virtual screening (VS), we synthesized seven novel derivatives of 1 that were identified in silico as being likely to have high binding affinity for the ERCC1-XPF heterodimerization interface by interacting with the XPF double helix-hairpin-helix (HhH2) domain. Two of the new compounds, 4-((6-chloro-2-methoxyacridin-9-yl)amino)-2-((4-cyclohexylpiperazin-1-yl)methyl)phenol (compound 3) and 4-((6-chloro-2-methoxyacridin-9-yl)amino)-2-((4-(2-(dimethylamino)ethyl) piperazin-1-yl) methyl) phenol (compound 4), were shown to be potent inhibitors of ERCC1-XPF activity in vitro. Compound 4 showed significant inhibition of the removal of CPDs in UV-irradiated cells and the capacity to sensitize colorectal cancer cells to UV radiation and cyclophosphamide.

Revealing the atomistic details behind the binding of B7-1 to CD28 and CTLA-4: A comprehensive protein-protein modelling study.

BACKGROUND: CD28 and CTLA-4 are homologous T-cell receptors that bind with B7-1 and produce two opposing immunological signals required for T-cell activation and inactivation, respectively. It has been clinically proven that specific blockade of these key protein-protein interactions at the synapse can offer immunotherapeutic benefits for cancers and autoimmune treatments. Hence, there is a growing interest towards developing anti-CD28 and anti-CTLA-4 small molecule inhibitors. To achieve this goal, it is important to understand unique molecular level fingerprint interactions that stabilize CTLA-4/B7-1 and CD28/B7-1 complexes. However, until recently, the structure of the human CD28/B7-1 complex has not been resolved experimentally, which remains a significant setback in achieving specific inhibitors against CTLA-4 or CD28. METHODS: Here, we employed a combination of advanced molecular modelling and extensive molecular dynamics (MD) simulations to model the CD28/B7-1 complex and characterize the key interactions that stabilize the complex. RESULTS: Ensemble protein-protein docking and MD-based binding-free energy calculations were used to obtain a comprehensive structural model of the CD28/B7-1 complex, which was validated with various mutation-based experimental data from literature. Our CD28/B7-1 model has much weaker binding affinity than the CTLA-4/B7-1 complex, which is in agreement with the results from our binding assay experiments and previous studies. CONCLUSIONS: Per-residue energy decomposition of the binding affinities of the two complexes revealed the unique fingerprint hot-spot sites in CTLA-4/B7-1 and CD28/B7-1 complexes. GENERAL SIGNIFICANCE: The results presented in this work will, on a long-run, be useful to develop new generation of specific CD28 and CTLA-4 inhibitors for targeted immunotherapy.

A mathematical modelling tool for unravelling the antibody-mediated effects on CTLA-4 interactions.

BACKGROUND: Monoclonal antibodies blocking the Cytotoxic T-lymphocyte antigen 4 (CTLA-4) receptor have revolutionized the field of anti-cancer therapy for the last few years. The human T-cell-based immune responses are modulated by two contradicting signals. CTLA-4 provides a T cell inhibitory signal through its interaction with B7 ligands (B7-1 and B7-2), while CD28 provides a stimulatory signal when interacting with the same ligands. A previous theoretical model has focused on understanding the processes of costimulatory and inhibitory complex formations at the synapse. Nevertheless, the effects of monoclonal antibody (mAb)-mediation on these complexes are relatively unexplored. In this work, we expand on the previous model to develop a new mathematical framework for studying the effects of anti-CTLA-4 mAbs on the co-stimulatory (CD28/B7 ligands) and the co-inhibitory (CTLA-4/B7 ligands) complex formation at the immunological synapse. In particular, we focus on two promising anti-CTLA-4 mAbs, tremelimumab (from AstraZeneca) and ipilimumab (from Bristol-Myers Squibb), which are currently in clinical trials and the market, respectively, for targeting multiple tumors. METHODS: The mathematical model in this work has been constructed based on ordinary differential equations and available experimental binding kinetics data for the anti-CTLA-4 antibodies from literature. RESULTS: The numerical simulations from the current model are in agreement with a number of experimental data. Especially, the dose-curves for blocking the B7 ligand binding to CTLA-4 by ipilimumab are comparable with the results from a previous competitive binding assay by flow cytometry and ELISA. Our simulations predict the dose response and the relative efficacies of the two mAbs in blocking the inhibitory CTLA-4/B7 complexes. CONCLUSIONS: The results show that different factors, such as multivalent interactions, mobility of molecules and competition effects, could impact the effects of antibody-mediation. The results, in particular, describe that the competitive effects could impact the dose-dependent inhibition by the mAbs very significantly. We present this model as a useful tool that can easily be translated to study the effects of any anti-CTLA-4 antibodies on immunological synaptic complex formation, provided reliable biophysical data for mAbs are available.

Computational Characterization of Small Molecules Binding to the Human XPF Active Site and Virtual Screening to Identify Potential New DNA Repair Inhibitors Targeting the ERCC1-XPF Endonuclease.

The DNA excision repair protein ERCC-1-DNA repair endonuclease XPF (ERCC1-XPF) is a heterodimeric endonuclease essential for the nucleotide excision repair (NER) DNA repair pathway. Although its activity is required to maintain genome integrity in healthy cells, ERCC1-XPF can counteract the effect of DNA-damaging therapies such as platinum-based chemotherapy in cancer cells. Therefore, a promising approach to enhance the effect of these therapies is to combine their use with small molecules, which can inhibit the repair mechanisms in cancer cells. Currently, there are no structures available for the catalytic site of the human ERCC1-XPF, which performs the metal-mediated cleavage of a DNA damaged strand at 5′. We adopted a homology modeling strategy to build a structural model of the human XPF nuclease domain which contained the active site and to extract dominant conformations of the domain using molecular dynamics simulations followed by clustering of the trajectory. We investigated the binding modes of known small molecule inhibitors targeting the active site to build a pharmacophore model. We then performed a virtual screening of the ZINC Is Not Commercial 15 (ZINC15) database to identify new ERCC1-XPF endonuclease inhibitors. Our work provides structural insights regarding the binding mode of small molecules targeting the ERCC1-XPF active site that can be used to rationally optimize such compounds. We also propose a set of new potential DNA repair inhibitors to be considered for combination cancer therapy strategies.

Effects of protein-protein interactions and ligand binding on the ion permeation in KCNQ1 potassium channel.

The voltage-gated KCNQ1 potassium ion channel interacts with the type I transmembrane protein minK (KCNE1) to generate the slow delayed rectifier (IKs) current in the heart. Mutations in these transmembrane proteins have been linked with several heart-related issues, including long QT syndromes (LQTS), congenital atrial fibrillation, and short QT syndrome. Off-target interactions of several drugs with that of KCNQ1/KCNE1 ion channel complex have been known to cause fatal cardiac irregularities. Thus, KCNQ1/KCNE1 remains an important avenue for drug-design and discovery research. In this work, we present the structural and mechanistic details of potassium ion permeation through an open KCNQ1 structural model using the combined molecular dynamics and steered molecular dynamics simulations. We discuss the processes and key residues involved in the permeation of a potassium ion through the KCNQ1 ion channel, and how the ion permeation is affected by (i) the KCNQ1-KCNE1 interactions and (ii) the binding of chromanol 293B ligand and its derivatives into the complex. The results reveal that interactions between KCNQ1 with KCNE1 causes a pore constriction in the former, which in-turn forms small energetic barriers in the ion-permeation pathway. These findings correlate with the previous experimental reports that interactions of KCNE1 dramatically slows the activation of KCNQ1. Upon ligand-binding onto the complex, the energy-barriers along ion permeation path are more pronounced, as expected, therefore, requiring higher force in our steered-MD simulations. Nevertheless, pulling the ion when a weak blocker is bound to the channel does not necessitate high force in SMD. This indicates that our SMD simulations have been able to discern between strong and week blockers and reveal their influence on potassium ion permeation. The findings presented here will have some implications in understanding the potential off-target interactions of the drugs with the KCNQ1/KCNE1 channel that lead to cardiotoxic effects.

Development of Safe Drugs: The hERG Challenge.

Drug-induced blockade of human ether-a-go-go-related gene (hERG) remains a major impediment in delivering safe drugs to the market. Several drugs have been withdrawn from the market due to their severe cardiotoxic side effects triggered by their off-target interactions with hERG. Thus, identifying the potential hERG blockers at early stages of lead discovery is fast evolving as a standard in drug design and development. A number of in silico structure-based models of hERG have been developed as a low-cost solution to evaluate drugs for hERG liability, and it is now agreed that the hERG blockers bind at the large central cavity of the channel. Nevertheless, there is no clear convergence on the appropriate drug binding modes against the channel. The proposed binding modes differ in their orientations and interpretations on the role of key residues in the channel. Such ambiguities in the modes of binding remain to be a significant challenge in achieving efficient computational predictive models and in saving many important already Food and Drug Administration approved drugs. In this review, we discuss the spectrum of reported binding modes for hERG blockers, the various in silico models developed for predicting a drug's affinity to hERG, and the known successful optimization strategies to avoid off-target interactions with hERG.

Modelling DNA Repair Pathways: Recent Advances and Future Directions.

BACKGROUND: A major class of chemotherapy drugs targets the genome of cancer cells. These DNA damaging agents induce damage to the DNA helix, resulting in the programmed death of cancer cells. An overactivated DNA repair mechanism in cancer cells can reduce the efficacy of these drugs, thereby eliminating their therapeutic benefit and developing an acquired resistance to these otherwise effective drugs. A promising approach to enhance the therapeutic window of DNA damaging agents is to target the DNA repair pathways causing this type of resistance. METHODS: Computational approaches have been applied successfully to study many of these DNA repair mechanisms at different scales and focusing on various aspects. The ultimate goal of these studies has been to identify the key players in developing resistance to DNA damaging agents and to design regulators for their activities. This review covers the most important and recent computational efforts toward this goal. This includes modelling the mechanisms involved in DNA repair and identifying novel pharmacological inhibitors for their activities. RESULTS: We focus here mainly on the pathways associated with an acquired drug resistance to DNA damaging agents, concentrating on the recent advances in modelling the key mechanisms and foreseeing the future directions in this field. CONCLUSION: We hope that this short, yet comprehensive review can help in discovering novel strategies to overcome the resistance effects inherent in various cancer treatments.

New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy.

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein-protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA-ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA-ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA-ERCC1 interaction.

Entropy in bimolecular simulations: A comprehensive review of atomic fluctuations-based methods.

Entropy of binding constitutes a major, and in many cases a detrimental, component of the binding affinity in biomolecular interactions. While the enthalpic part of the binding free energy is easier to calculate, estimating the entropy of binding is further more complicated. A precise evaluation of entropy requires a comprehensive exploration of the complete phase space of the interacting entities. As this task is extremely hard to accomplish in the context of conventional molecular simulations, calculating entropy has involved many approximations. Most of these golden standard methods focused on developing a reliable estimation of the conformational part of the entropy. Here, we review these methods with a particular emphasis on the different techniques that extract entropy from atomic fluctuations. The theoretical formalisms behind each method is explained highlighting its strengths as well as its limitations, followed by a description of a number of case studies for each method. We hope that this brief, yet comprehensive, review provides a useful tool to understand these methods and realize the practical issues that may arise in such calculations.

Discovery of Novel Peptidomimetics as Irreversible CHIKV NsP2 Protease Inhibitors Using Quantum Mechanical-Based Ligand Descriptors.

Chikungunya virus (CHIKV) is a mosquito-borne alphavirus. Recent outbreaks of CHIKV infections have been reported in Asia, Africa, and Europe. The symptoms of CHIKV infection include fever, headache, nausea, vomiting, myalgia, rash, and chronic persistent arthralgia. To date, no vaccines or selective antiviral drugs against this important emerging virus have been reported. In this study, the design, synthesis, and antiviral activity screening of new topographical peptidomimetics revealed three potential prototype agents 3a, 4b, and 5d showing 93-100% maximum inhibition of CHIKV replication in cell-based assay having EC90 of 8.76-9.57 µg/mL. Intensive molecular modeling studies including covalent docking, lowest unoccupied molecular orbital energies, and the atomic condensed Fukui functions calculations strongly suggested the covalent binding of peptidomimetics 3a, 4b, and 5d to CHIKV nsP2 protease leading to permanent enzyme inactivation via Michael adduct formation between α/ß-unsaturated ketone functionality in our designed peptidomimetics and active site catalytic cysteine1013. Furthermore, small molecular weight peptidomimetics 3a and 4b satisfied the Lipinski rule of five for drug-likeness and showed promising intestinal absorption and aqueous solubility via computational admet studies making them promising hits for further optimization.

A Refined Model of the HCV NS5A protein bound to daclatasvir explains drug-resistant mutations and activity against divergent genotypes.

Many direct-acting antiviral agents (DAAs) that selectively block hepatitis C virus (HCV) replication are currently under development. Among these agents is Daclatasvir, a first-in-class inhibitor targeting the NS5A viral protein. Although Daclatasvir is the most potent HCV antiviral molecule yet developed, its binding location and mode of binding remain unknown. The drug exhibits a low barrier to resistance mutations, particularly in genotype 1 viruses, but its efficacy against other genotypes is unclear. Using state-of-the-art modeling techniques combined with the massive computational power of Blue Gene/Q, we identified the atomic interactions of Daclatasvir within NS5A for different HCV genotypes and for several reported resistant mutations. The proposed model is the first to reveal the detailed binding mode of Daclatasvir. It also provides a tool to facilitate design of second generation drugs, which may confer less resistance and/or broader activity against HCV.

A human ether-á-go-go-related (hERG) ion channel atomistic model generated by long supercomputer molecular dynamics simulations and its use in predicting drug cardiotoxicity.

Acquired cardiac long QT syndrome (LQTS) is a frequent drug-induced toxic event that is often caused through blocking of the human ether-á-go-go-related (hERG) K(+) ion channel. This has led to the removal of several major drugs post-approval and is a frequent cause of termination of clinical trials. We report here a computational atomistic model derived using long molecular dynamics that allows sensitive prediction of hERG blockage. It identified drug-mediated hERG blocking activity of a test panel of 18 compounds with high sensitivity and specificity and was experimentally validated using hERG binding assays and patch clamp electrophysiological assays. The model discriminates between potent, weak, and non-hERG blockers and is superior to previous computational methods. This computational model serves as a powerful new tool to predict hERG blocking thus rendering drug development safer and more efficient. As an example, we show that a drug that was halted recently in clinical development because of severe cardiotoxicity is a potent inhibitor of hERG in two different biological assays which could have been predicted using our new computational model.