scholarly journals Adapting the DeepSARM approach for dual-target ligand design

Author(s):  
Atsushi Yoshimori ◽  
Huabin Hu ◽  
Jürgen Bajorath

AbstractThe structure–activity relationship (SAR) matrix (SARM) methodology and data structure was originally developed to extract structurally related compound series from data sets of any composition, organize these series in matrices reminiscent of R-group tables, and visualize SAR patterns. The SARM approach combines the identification of structural relationships between series of active compounds with analog design, which is facilitated by systematically exploring combinations of core structures and substituents that have not been synthesized. The SARM methodology was extended through the introduction of DeepSARM, which added deep learning and generative modeling to target-based analog design by taking compound information from related targets into account to further increase structural novelty. Herein, we present the foundations of the SARM methodology and discuss how DeepSARM modeling can be adapted for the design of compounds with dual-target activity. Generating dual-target compounds represents an equally attractive and challenging task for polypharmacology-oriented drug discovery. The DeepSARM-based approach is illustrated using a computational proof-of-concept application focusing on the design of candidate inhibitors for two prominent anti-cancer targets.

F1000Research ◽  
2014 ◽  
Vol 3 ◽  
pp. 113 ◽  
Author(s):  
Disha Gupta-Ostermann ◽  
Jürgen Bajorath

We describe the ‘Structure-Activity Relationship (SAR) Matrix’ (SARM) methodology that is based upon a special two-step application of the matched molecular pair (MMP) formalism. The SARM method has originally been designed for the extraction, organization, and visualization of compound series and associated SAR information from compound data sets. It has been further developed and adapted for other applications including compound design, activity prediction, library extension, and the navigation of multi-target activity spaces. The SARM approach and its extensions are presented here in context to introduce different types of applications and provide an example for the evolution of a computational methodology in pharmaceutical research.


2019 ◽  
Vol 19 (7) ◽  
pp. 842-874 ◽  
Author(s):  
Harbinder Singh ◽  
Nihar Kinarivala ◽  
Sahil Sharma

We live in a world with complex diseases such as cancer which cannot be cured with one-compound one-target based therapeutic paradigm. This could be due to the involvement of multiple pathogenic mechanisms. One-compound-various-targets stratagem has become a prevailing research topic in anti-cancer drug discovery. The simultaneous interruption of two or more targets has improved the therapeutic efficacy as compared to the specific targeted based therapy. In this review, six types of dual targeting agents along with some interesting strategies used for their design and synthesis are discussed. Their pharmacology with various types of the molecular interactions within their specific targets has also been described. This assemblage will reveal the recent trends and insights in front of the scientific community working in dual inhibitors and help them in designing the next generation of multi-targeted anti-cancer agents.


Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format


Author(s):  
Thomas Blaschke ◽  
Jürgen Bajorath

AbstractExploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A469-A469
Author(s):  
Bernard Fox ◽  
Tarsem Moudgil ◽  
Traci Hilton ◽  
Noriko Iwamoto ◽  
Christopher Paustian ◽  
...  

BackgroundOutcomes for recurrent or metastatic (R/M) head and neck squamous cell carcinoma (HNSCC) are dismal and responses to anti-PD-1 appear best in tumors with PD-1+ T cells in proximity to PD-L1+ cells, arguing that improved outcome is associated with a pre-existing anti-cancer immune response. Based on this, we hypothesize that vaccines which prime and/or expand T cells to a spectrum of antigens overexpressed by HNSCC combined with T cell agonists, like anti-GITR, that provide costimulatory signals will improve the anti-PD-1 response rates. We have developed a cancer vaccine, DPV-001, that contains more than 300 proteins for genes overexpressed by HNSCC, encapsulated in a CLEC9A-targeted microvesicle and containing TLR/NOD agonists and DAMPs. Recently, we reported that combining anti-GITR + vaccine + anti-PD-1 augmented therapeutic efficacy in a preclinical model and now plan a phase 1b trial of this combination in patients with advanced HNSCC.MethodsSera from patients receiving DPV-001 as adjuvant therapy for definitively treated NSCLC, were analyzed for IgG responses to human proteins by MAP bead arrays and results compared to TCGA gene expression data sets for HNSCC. HNSCC cell lines were evaluated by RNASeq and peptides were eluted from HLA, analyzed by mass spectroscopy and correlated against MAP bead arrays and TCGA data sets. Tumor-reactive T cells from a vaccinated patient were enriched and expanded, and used in cytokine release assay (CRA) against autologous NSCLC and partially HLA matched allogeneic HNSCC cell lines.ResultsPatients receiving DPV-001 (N=13) made 147 IgG responses to at least 70 proteins for genes overexpressed by HNSCC. Preliminary evaluation of the HNSCC peptidome against the results of MAP bead array identify antigens that are target of a humoral immune response. Additionally, tumor-reactive T cells from DPV-001 vaccinated patient recognize two partially HLA-matched HNSCC targets, but not a mis-matched target.ConclusionsRecent observations from our lab and others have correlated IgG Ab responses with T cell responses to epitopes of the same protein. Based on the data summarized above, we hypothesize that we have induced T cell responses against a broad spectrum of shared cancer antigens that are common among adenocarcinomas and squamous cell cancers. Our planned clinical trial will vaccinate and boost the induced responses by costimulation with anti-GITR and then sequence in delayed anti-PD-1 to relieve checkpoint inhibition. MAP bead arrays and the peptidome library generated above will be used to assess anti-cancer B and T cell responses.Trial RegistrationNCT04470024Ethics ApprovalThe original clinical trial was approved by the Providence Portland Medical Center IRB, approval # 13-046. The proposed clinical trial has not yet been reviewed by the IRB.


PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e60074 ◽  
Author(s):  
Chapla Agarwal ◽  
Ritambhara Wadhwa ◽  
Gagan Deep ◽  
David Biedermann ◽  
Radek Gažák ◽  
...  

2020 ◽  
Author(s):  
Alessio Ciulli ◽  
Satomi Imaide ◽  
Kristin Riching ◽  
Vesna Vetma ◽  
Claire Whitworth ◽  
...  

Abstract Bivalent small-molecule degraders, or proteolysis targeting chimeras (PROTACs), work by simultaneously binding a target protein and E3 ubiquitin ligase to produce a ternary complex. To drive target ubiquitination and degradation at low catalytic concentrations, degraders must form appropriately positioned complexes of sufficient stability, aided by intra-complex interactions. We hypothesized these molecular recognition features could be enhanced by increasing binding valency. Here we present trivalent PROTACs as a strategy to boost protein degradation. Our design for a trivalent PROTAC consisted of two BET bromodomain inhibitors and an E3 ligase ligand, each separately tethered via a branched linker. In screening, we identified SIM1, a VHL-based PROTAC, as a highly potent BET degrader, capable of low picomolar degradation for all family members, with preference for BRD2. In functional comparison studies to bivalent PROTACs or inhibitors, SIM1 showed more sustained anti-cancer activity across numerous therapeutically relevant cell lines. Biophysical, biochemical, and cellular mechanistic studies showed SIM1 induces conformational changes upon binding to the BET protein to simultaneously engage with high avidity both its bromodomains in a cis intramolecular fashion. The resulting 1:1:1 complex showed positive cooperativity, high stability and prolonged cellular residence time. We provide proof-of-concept for augmenting the binding valency of proximity-induced modalities as a strategy to leverage both cooperativity and avidity within the ternary complex to advance functional outcomes.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 11-11
Author(s):  
Andrej Besse ◽  
Lenka Besse ◽  
Sara C. Stolze ◽  
Amin Sobh ◽  
Esther A. Zaal ◽  
...  

INTRODUCTION Nelfinavir is a highly lipophilic, first generation HIV-protease inhibitor (HIV-PI) approved for HIV treatment. It has largely been replaced by next-generation HIV-PI with increased specificity and efficacy for HIV therapy, partly reflecting the significant rate of the off-target activity of nelfinavir. Increasing preclinical and clinical evidence shows that nelfinavir has broad anti-cancer activity as a single agent and in combination, potentially related to its off-target activity in mammalian cells. Nelfinavir is particularly effective in the treatment of proteasome inhibitor-refractory multiple myeloma (MM), where the combination of nelfinavir+bortezomib+dexamethasone yielded an overall response rate (ORR, PR or better) > 65% in a Phase II clinical trial. The targets and molecular mechanism of action of nelfinavir in MM are unknown. This hampers both, a rational clinical repositioning and development of nelfinavir as antineoplastic drug, as well as the design, synthesis and testing of next generation nelfinavir-like compounds with optimized antineoplastic activity and improved specificity or pharmacologic properties. We therefore aimed to take an unbiased target-identification approach to identify molecular targets of nelfinavir in human malignant cells and link them to cell biological processes and mechanisms that mediate sensitivity or resistance to nelfinavir treatment. METHODS Proteome-wide affinity-purification of targets binding the nelfinavir active site was combined with genome-wide CRISPR/Cas9-based screening to identify protein partners interacting with nelfinavir and candidate genetic contributors affecting nelfinavir cytotoxicity. Multiple intracellular reporter systems including RUSH system, ATP/ADP constructs; FRAP microscopy, Seahorse measurements, flow cytometry, qPCR, metabolic labelling, lipidomics and viability assays were used to dissect functional alterations in pathways related to nelfinavir targets. RESULTS We identified a common set of proteins interacting specifically with the active site of nelfinavir. These proteins are embedded in intracellular, lipid-rich membranes of mitochondria (VDAC1,2,3, ANT2), endoplasmic reticulum (BCAP31, CANX, SRPRB) and nuclear envelope (PGRMC2) and are consistent across multiple cancer cell types. ADIPOR2, a key regulator gene of membrane lipid fluidity, was identified as a key nelfinavir resistance gene, while genes involved in fatty acids (FAs) and cholesterol metabolism, vesicular trafficking and mitochondria biogenesis are candidate sensitivity genes. We further show that via binding to proteins in lipid-rich membranes nelfinavir affects membrane composition and reduces membrane fluidity, leading to induction of FAs synthesis and the unfolded protein response (UPR). Via its structural interference with membrane fluidity, nelfinavir impairs the function and mobility of a diverse set of membrane-associated proteins and processes, such as glucose flux and processing, mitochondria respiration, energy supply, transmembrane vesicular transport and ABCB1-mediated drug efflux, as we show in different reporter systems in live MM cells. These functional effects are prevented by addition of metabolically inert lipids to be incorporated in membranes, supporting a direct structural activity of nelfinavir. The adaptive biology of proteasome inhibitor (PI)-resistant myeloma relies on metabolic reprogramming and changes in lipid composition, drug export and down-modulation of the UPR. Modulation of membrane fluidity and depletion of FAs/cholesterol is synergistic with proteasome inhibitors in PI-resistant MM. Thus, the mechanism of action of nelfinavir perfectly matches with the biology of PI-resistant MM, serving as a molecular rational for its significant clinical activity. CONCLUSION We here demonstrate in vitro that the activity of nelfinavir against MM cells is triggered through changes in lipid metabolism and the fluidity of lipid-rich membranes. Pharmacologic targeting of membrane fluidity is a novel, potent mechanism to achieve anti-cancer activity, in particular against PI-refractory MM. This mechanism explains the clinical activity of nelfinavir in MM treatment as well as the key side effects of nelfinavir during antiretroviral therapy. Disclosures No relevant conflicts of interest to declare.


F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 75 ◽  
Author(s):  
Disha Gupta-Ostermann ◽  
Yoichiro Hirose ◽  
Takenao Odagami ◽  
Hiroyuki Kouji ◽  
Jürgen Bajorath

In a previous Method Article, we have presented the ‘Structure-Activity Relationship (SAR) Matrix’ (SARM) approach. The SARM methodology is designed to systematically extract structurally related compound series from screening or chemical optimization data and organize these series and associated SAR information in matrices reminiscent of R-group tables. SARM calculations also yield many virtual candidate compounds that form a “chemical space envelope” around related series. To further extend the SARM approach, different methods are developed to predict the activity of virtual compounds. In this follow-up contribution, we describe an activity prediction method that derives conditional probabilities of activity from SARMs and report representative results of first prospective applications of this approach.


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