Kinome-Wide Virtual Screening by Multi-Task Deep Learning.

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods.

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Sagittal mechanical ratio: A novel technique to define sagittal alignment of the femur independent of distal anatomic landmarks.

BACKGROUND: Radiographic measurements to study sagittal alignment in the setting of knee are frequently difficult to evaluate due the presence of a prosthesis or implant that obscures traditional radiographic landmarks. In this paper we present a novel method of determining sagittal femoral alignment in the presence of obscuring implants. METHODS: 98 full-length femoral radiographs were reviewed and divided into two groups. In Group 1, the Distal Mechanical Point (DMP) was used to calculate the Distal Mechanical Ratio (DMR), defined as the ratio of the linear distance from the DMP to the anterior cortical axis divided by the distance from the anterior cortical axis to posterior condylar cortex. In group 2, the sagittal mechanical axis was measured using the true DMP (tDMP) and then separately measured using the DMR to find the calculated DMP (cDMP), and the angular variance between the calculated (cSMA) and true (tSMA) sagittal mechanical axis was calculated, as well as the linear distance between the tDMP and cDMP. Twenty additional patients with knee replacements were then selected and two observers used a cSMA to determine a femoral prosthesis flexion angle (FPFA), with intraobserver correlation calculated. RESULTS: The mean DMR was found to be 0.24, with high intraobserver correlation and normal distribution. Validation of the model demonstrated angular variance between tSMA and cSMA less than 1 degree and linear distance between tDMP and cDMP less than 1 mm. Calculation of cCMA in the presence of total knee arthroplasty revealed very strong intraobserver correlation of 0.89. CONCLUSION: The Distal Mechanical Ratio reliably predicted the true Sagittal Mechanical Axis within 1 degree and true Distal Mechanical Point within 1 mm, indicating that it may be a valuable tool for evaluating sagittal femoral alignment in cases where anatomic landmarks may be absent or obscured.

Incision site fat thickness and 90-day complications for direct anterior and posterior approach total hip arthroplasty.

BACKGROUND: Defining the distribution of subcutaneous fat around the hip in relation to different approaches for total hip arthroplasty (THA) may lead to a better understanding of the relationship between obesity and complications. The purpose of this study was to: (1) describe the intraoperative thickness of subcutaneous fat at the incision site for direct anterior (DAA) and posterior approaches (PA) for THA; and (2) examine the relationship between fat thickness and 90-day postoperative complications. METHODS: Intraoperative fat measurements were obtained at the anterior incision site (AT-IS) of the DAA (n = 60) and the lateral incision site (LT-IS) of the PA (n = 64). Lateral hip fat thickness was measured from preoperative anteroposterior pelvis radiographs (LT-XR). Body mass index (BMI), sex, age, and 90-day complications were collected retrospectively. RESULTS: Patients within the same demographic groupings had significantly more fat laterally than anteriorly, between 9.6 mm and 17.96 mm. Return to the OR was significantly associated with BMI, AT-IS, and LT-IS. Wound complications were significantly associated with AT-IS. Periprosthetic joint infection (PJI) was significantly associated with BMI and LT-IS. No outcome variables were associated with LT-XR, approach, sex, or age. LT-XR was strongly correlated with AT-IS and LT-IS. CONCLUSIONS: Regardless of BMI, sex, or age more soft tissue was encountered with a PA compared to a DAA. General adiposity was associated with return to the OR. Excess incisional fat was associated with wound complications following a DAA and PJI after a PA. LT-XR and clinical examination near the proposed incision, may provide helpful data in making preoperative risk assessments.

Clinical Pathways of Patients Denied Total Knee Arthroplasty Due to an Institutional BMI Cutoff.

Out of concern for the increased risk of complications with morbid obesity, institutional body mass index (BMI) cutoffs for total knee arthroplasty (TKA) have become commonplace. We sought to answer the questions: what percentage of morbidly obese patients with knee osteoarthritis who present to an arthroplasty clinic will, within 2 years, undergo TKA at (1) a BMI less than 40 kg/m2 or (2) at a BMI greater than 40 kg/m2? Of those who do not undergo surgery, (3) what percentage lose enough weight to become TKA-eligible, and (4) what percentage do not? We performed an observational study of 288 patients, of which 256 had complete follow-up. Institutional electronic medical record review and patient follow-up by telephone were conducted to determine which patients underwent surgery, and at what BMI. For those that did not undergo TKA, BMI was examined to see if the patient ever lost enough weight to become TKA eligible. Twelve of 256 patients (4.7%) underwent TKA at a BMI less than 40 kg/m2, 64 patients (25%) underwent TKA at a BMI greater than 40 kg/m2, and 7 patients (2.7%) underwent surgery at an outside hospital. The average BMI at the time of surgery was 42.3 kg/m2. Thirty-seven of 256 patients (14.4%) lost enough weight to become TKA-eligible within 2 years of the initial visit but did not undergo surgery, while 136 patients (53.1%) neither underwent TKA nor became eligible. Strict enforcement of a BMI cutoff for TKA is variable among surgeons. In the absence of weight loss protocols, 19.1% of morbidly obese patients may be expected to reach the sub-40 kg/m2 BMI milestone.

Precise Binding Free Energy Calculations for Multiple Molecules Using an Optimal Measurement Network of Pairwise Differences.

Alchemical binding free energy (BFE) calculations offer an efficient and thermodynamically rigorous approach to in silico binding affinity predictions. As a result of decades of methodological improvements and recent advances in computer technology, alchemical BFE calculations are now widely used in drug discovery research. They help guide the prioritization of candidate drug molecules by predicting their binding affinities for a biomolecular target of interest (and potentially selectivity against undesirable antitargets). Statistical variance associated with such calculations, however, may undermine the reliability of their predictions, introducing uncertainty both in ranking candidate molecules and in benchmarking their predictive accuracy. Here, we present a computational method that substantially improves the statistical precision in BFE calculations for a set of ligands binding to a common receptor by dynamically allocating computational resources to different BFE calculations according to an optimality objective established in a previous work from our group and extended in this work. Our method, termed Network Binding Free Energy (NetBFE), performs adaptive BFE calculations in iterations, re-optimizing the allocations in each iteration based on the statistical variances estimated from previous iterations. Using examples of NetBFE calculations for protein binding of congeneric ligand series, we demonstrate that NetBFE approaches the optimal allocation in a small number (≤5) of iterations and that NetBFE reduces the statistical variance in the BFE estimates by approximately a factor of 2 when compared to a previously published and widely used allocation method at the same total computational cost.

Role of inflammatory markers in the preoperative evaluation of patients undergoing salvage total hip arthroplasty.

Surgeons may use laboratory tests, including erythrocyte sedimentation rate, C-reactive protein (CRP), and white blood cell count, as well as joint aspirations to diagnose prosthetic joint infections. There is a paucity of literature correlating preoperative inflammatory markers with risk of infection in the setting of salvage total hip arthroplasty (THA). This retrospective case analysis included patients who underwent a THA salvage procedure a minimum of 3 months after a failed fixation of a proximal femur or acetabulum, with a goal of assessing the utility of inflammatory markers as a screening tool in preoperative evaluation of salvage THA. Eighty-five patients met inclusion criteria. Thirteen patients were diagnosed with an infection preoperatively or intraoperatively during salvage THA. An elevated preoperative CRP level was a significant marker for infection. A CRP of 7.1 produced 80% sensitivity, 88% specificity, and a receiver operating characteristic curve of 0.840. There was a high rate of perioperative complications (17.6%) in salvage THA regardless of the presence of infection. In conclusion, CRP levels are useful in the preoperative evaluation for periprosthetic joint infection before salvage THA.

Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Predicting protein-ligand binding affinities and the associated thermodynamics of biomolecular recognition is a primary objective of structure-based drug design. Alchemical free energy simulations offer a highly accurate and computationally efficient route to achieving this goal. While the AMBER molecular dynamics package has successfully been used for alchemical free energy simulations in academic research groups for decades, widespread impact in industrial drug discovery settings has been minimal because of the previous limitations within the AMBER alchemical code, coupled with challenges in system setup and postprocessing workflows. Through a close academia-industry collaboration we have addressed many of the previous limitations with an aim to improve accuracy, efficiency, and robustness of alchemical binding free energy simulations in industrial drug discovery applications. Here, we highlight some of the recent advances in AMBER20 with a focus on alchemical binding free energy (BFE) calculations, which are less computationally intensive than alternative binding free energy methods where full binding/unbinding paths are explored. In addition to scientific and technical advances in AMBER20, we also describe the essential practical aspects associated with running relative alchemical BFE calculations, along with recommendations for best practices, highlighting the importance not only of the alchemical simulation code but also the auxiliary functionalities and expertise required to obtain accurate and reliable results. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative BFE simulations in AMBER20, with a focus on real-world drug discovery applications.

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials.

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.

Predictive value of lateral soft tissue thickness for complications after total hip arthroplasty with a lateral incision.

The purpose of this study was to determine the relationship between soft tissue thickness lateral to the greater trochanter, as measured on anteroposterior pelvis radiograph, and postoperative complications following primary total hip arthroplasty. A retrospective review of 1110 consecutive patients treated at a single institution from 2003 to 2011 was conducted. Postoperative complications were divided into surgical site infections, deep wound infections, noninfectious surgical complications, need for revision surgery, and medical complications. Lateral soft tissue thickness (LSTT) was measured as the horizontal distance from the most lateral point on the greater trochanter to the skin edge obtained from anteroposterior hip radiographs. Among the 1110 study patients, 19.19% had a postoperative complication, with a deep infection rate of 3.42%. Of the previously identified risk factors, increased LSTT and body mass index were both associated with surgical site infection and deep infection, and LSTT was associated with revision surgery. An LSTT value of >5 cm was predictive of surgical site infection, deep infection, and revision surgery. This easily obtainable radiographic measurement, along with clinical examination near the operative site, might prove helpful in making preoperative risk assessments.

Rigorous Free Energy Simulations in Virtual Screening.

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Best Practices for Alchemical Free Energy Calculations [Article v1.0].

Alchemical free energy calculations are a useful tool for predicting free energy differences associated with the transfer of molecules from one environment to another. The hallmark of these methods is the use of "bridging" potential energy functions representing alchemical intermediate states that cannot exist as real chemical species. The data collected from these bridging alchemical thermodynamic states allows the efficient computation of transfer free energies (or differences in transfer free energies) with orders of magnitude less simulation time than simulating the transfer process directly. While these methods are highly flexible, care must be taken in avoiding common pitfalls to ensure that computed free energy differences can be robust and reproducible for the chosen force field, and that appropriate corrections are included to permit direct comparison with experimental data. In this paper, we review current best practices for several popular application domains of alchemical free energy calculations performed with equilibrium simulations, in particular relative and absolute small molecule binding free energy calculations to biomolecular targets.

Data Portal for the Library of Integrated Network-based Cellular Signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data.

The Library of Integrated Network-based Cellular Signatures (LINCS) program is a national consortium funded by the NIH to generate a diverse and extensive reference library of cell-based perturbation-response signatures, along with novel data analytics tools to improve our understanding of human diseases at the systems level. In contrast to other large-scale data generation efforts, LINCS Data and Signature Generation Centers (DSGCs) employ a wide range of assay technologies cataloging diverse cellular responses. Integration of, and unified access to LINCS data has therefore been particularly challenging. The Big Data to Knowledge (BD2K) LINCS Data Coordination and Integration Center (DCIC) has developed data standards specifications, data processing pipelines, and a suite of end-user software tools to integrate and annotate LINCS-generated data, to make LINCS signatures searchable and usable for different types of users. Here, we describe the LINCS Data Portal (LDP) (http://lincsportal.ccs.miami.edu/), a unified web interface to access datasets generated by the LINCS DSGCs, and its underlying database, LINCS Data Registry (LDR). LINCS data served on the LDP contains extensive metadata and curated annotations. We highlight the features of the LDP user interface that is designed to enable search, browsing, exploration, download and analysis of LINCS data and related curated content.

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Identification of a Novel Class of BRD4 Inhibitors by Computational Screening and Binding Simulations.

Computational screening is a method to prioritize small-molecule compounds based on the structural and biochemical attributes built from ligand and target information. Previously, we have developed a scalable virtual screening workflow to identify novel multitarget kinase/bromodomain inhibitors. In the current study, we identified several novel N-[3-(2-oxo-pyrrolidinyl)phenyl]-benzenesulfonamide derivatives that scored highly in our ensemble docking protocol. We quantified the binding affinity of these compounds for BRD4(BD1) biochemically and generated cocrystal structures, which were deposited in the Protein Data Bank. As the docking poses obtained in the virtual screening pipeline did not align with the experimental cocrystal structures, we evaluated the predictions of their precise binding modes by performing molecular dynamics (MD) simulations. The MD simulations closely reproduced the experimentally observed protein-ligand cocrystal binding conformations and interactions for all compounds. These results suggest a computational workflow to generate experimental-quality protein-ligand binding models, overcoming limitations of docking results due to receptor flexibility and incomplete sampling, as a useful starting point for the structure-based lead optimization of novel BRD4(BD1) inhibitors.

Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders.

Linking putatively pathogenic variants to the tissues they affect is necessary for determining the correct diagnostic workup and therapeutic regime in undiagnosed patients. Here, we explored how gene expression across healthy tissues can be used to infer this link. We integrated 6,665 tissue-wide transcriptomes with genetic disorder knowledge bases covering 3,397 diseases. Receiver-operating characteristics (ROC) analysis using expression levels in each tissue and across tissues indicated significant but modest associations between elevated expression and phenotype for most tissues (maximum area under ROC curve = 0.69). At extreme elevation, associations were marked. Upregulation of disease genes in affected tissues was pronounced for genes associated with autosomal dominant over recessive disorders. Pathways enriched for genes expressed and associated with phenotypes highlighted tissue functionality, including lipid metabolism in spleen and DNA repair in adipose tissue. These results suggest features useful for evaluating the likelihood of particular tissue manifestations in genetic disorders. The web address of an interactive platform integrating these data is provided.

Dry Needling Versus Cortisone Injection in the Treatment of Greater Trochanteric Pain Syndrome: A Noninferiority Randomized Clinical Trial.

Study Design Prospective, randomized, partially blinded. Background Greater trochanteric pain syndrome (GTPS) is the current terminology for what was once called greater trochanteric or subgluteal bursitis. Cortisone (corticosteroid) injection into the lateral hip has traditionally been the accepted treatment for this condition; however, the effectiveness of injecting the bursa with steroids is increasingly being questioned. An equally effective treatment with fewer adverse side effects would be beneficial. Objective To investigate whether administration of dry needling (DN) is noninferior to cortisone injection in reducing lateral hip pain and improving function in patients with GTPS. Methods Forty-three participants (50 hips observed), all with GTPS, were randomly assigned to a group receiving cortisone injection or DN. Treatments were administered over 6 weeks, and clinical outcomes were collected at baseline and at 1, 3, and 6 weeks. The primary outcome measure was the numeric pain-rating scale (0-10). The secondary outcome measure was the Patient-Specific Functional Scale (0-10). Medication intake for pain was collected as a tertiary outcome. Results Baseline characteristics were similar between groups. A noninferiority test for a repeated-measures design for pain and averaged function scores at 6 weeks (with a noninferiority margin of 1.5 for both outcomes) indicated noninferiority of DN versus cortisone injection (both, P<.01). Medication usage (P = .74) was not different between groups at the same time point. No adverse side effects were reported. Conclusion Cortisone injections for GTPS did not provide greater pain relief or reduction in functional limitations than DN. Our data suggest that DN is a noninferior treatment alternative to cortisone injections in this patient population. Level of Evidence Therapy, level 1b. Registered December 2, 2015 at www.clinicaltrials.gov (NCT02639039). J Orthop Sports Phys Ther 2017;47(4):232-239. Epub 3 Mar 2017. doi:10.2519/jospt.2017.6994.

Distribution of Subcutaneous Fat Around the Hip in Relation to Surgical Approach for Total Hip Arthroplasty.

BACKGROUND: Although preoperative risk assessment is multifactorial, subcutaneous fat thickness at the incision site has been associated with postoperative complications in studies of total hip arthroplasty (THA) and other procedures. This study compared subcutaneous fat thickness encountered in THA approaches using a lateral hip incision with that of approaches using an anterior incision and examined the relationship between body mass index (BMI) and fat distribution based on sex and age. METHODS: Subcutaneous fat measurements were obtained from 2004 patient CT images at positions that correspond with lateral and anterior incision sites for common approaches to THA. A thickness ratio (lateral/anterior) was calculated, and BMI, sex, and age were collected via chart review. RESULTS: Males and females had significantly different thickness ratio averages at 1.97 and 2.68, respectively. Thickness ratios were not significantly different between BMI groups. Lateral thickness averages were significantly different for males and females, and the interaction between sex and BMI group was significant. The relationship between BMI and the thickness ratio in males aged ≥65 years was significantly different from males of <65 years and females of all ages. CONCLUSION: Regardless of BMI, sex, or age, incision site soft tissue thickness was greater for approaches using a lateral hip incision than for those with an anterior incision, and a positive relationship between BMI and both measurements was identified. The predominance of lateral fat was more pronounced in females of all age and BMI groups and less pronounced in obese males aged ≥65 years.

Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4.

Inhibition of cancer-promoting kinases is an established therapeutic strategy for the treatment of many cancers, although resistance to kinase inhibitors is common. One way to overcome resistance is to target orthogonal cancer-promoting pathways. Bromo and Extra-Terminal (BET) domain proteins, which belong to the family of epigenetic readers, have recently emerged as promising therapeutic targets in multiple cancers. The development of multitarget drugs that inhibit kinase and BET proteins therefore may be a promising strategy to overcome tumor resistance and prolong therapeutic efficacy in the clinic. We developed a general computational screening approach to identify novel dual kinase/bromodomain inhibitors from millions of commercially available small molecules. Our method integrated machine learning using big datasets of kinase inhibitors and structure-based drug design. Here we describe the computational methodology, including validation and characterization of our models and their application and integration into a scalable virtual screening pipeline. We screened over 6 million commercially available compounds and selected 24 for testing in BRD4 and EGFR biochemical assays. We identified several novel BRD4 inhibitors, among them a first in class dual EGFR-BRD4 inhibitor. Our studies suggest that this computational screening approach may be broadly applicable for identifying dual kinase/BET inhibitors with potential for treating various cancers.

Epigenetic pathways and glioblastoma treatment: insights from signaling cascades.

There is an urgent need to identify novel therapies for glioblastoma (GBM) as most therapies are ineffective. A first step in this process is to identify and validate targets for therapeutic intervention. Epigenetic modulators have emerged as attractive drug targets in several cancers including GBM. These epigenetic regulators affect gene expression without changing the DNA sequence. Recent studies suggest that epigenetic regulators interact with drivers of GBM cell and stem-like cell proliferation. These drivers include components of the Notch, Hedgehog, and Wingless (WNT) pathways. We highlight recent studies connecting epigenetic and signaling pathways in GBM. We also review systems and big data approaches for identifying patient specific therapies in GBM. Collectively, these studies will identify drug combinations that may be effective in GBM and other cancers.