Accelerated Chemical Reaction Optimization using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations need to be executed in the presence of polar functionality necessary for fragment-protein binding. New technologies such as high-throughput experimentation and self-optimization have the potential to revolutionize synthetic approaches to challenging target molecules in FBDD. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions, however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multi-task Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected during related historical optimization campaigns to accelerate the optimization of new reactions - this was performed for Suzuki-Miyaura and C–N couplings. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions (both continuous and categorical variables) of unseen experimental C–H activation reactions with differing substrates, demonstrating up to a 98 % cost reduction when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, where efficient utilization of precious starting materials is particularly important. This work represents a step-change in the utilization of previously obtained reaction data and machine learning with the ultimate goal of accelerated reaction optimization

Accelerated Chemical Reaction Optimization Using Multi-Task Learning.

Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization

PTEN deficiency promotes macrophage infiltration and hypersensitivity of prostate cancer to IAP antagonist/radiation combination therapy

PTEN loss is prognostic for patient relapse post-radiotherapy in prostate cancer (CaP). Infiltration of tumor-associated macrophages (TAMs) is associated with reduced disease-free survival following radical prostatectomy. However, the association between PTEN loss, TAM infiltration and radiotherapy response of CaP cells remains to be evaluated. Immunohistochemical and molecular analysis of surgically-resected Gleason 7 tumors confirmed that PTEN loss correlated with increased CXCL8 expression and macrophage infiltration. However PTEN status had no discernable correlation with expression of other inflammatory markers by CaP cells, including TNF-α. In vitro, exposure to conditioned media harvested from irradiated PTEN null CaP cells induced chemotaxis of macrophage-like THP-1 cells, a response partially attenuated by CXCL8 inhibition. Co-culture with THP-1 cells resulted in a modest reduction in the radio-sensitivity of DU145 cells. Cytokine profiling revealed constitutive secretion of TNF-α from CaP cells irrespective of PTEN status and IR-induced TNF-α secretion from THP-1 cells. THP-1-derived TNF-α increased NFκB pro-survival activity and elevated expression of anti-apoptotic proteins including cellular inhibitor of apoptosis protein-1 (cIAP-1) in CaP cells, which could be attenuated by pre-treatment with a TNF-α neutralizing antibody. Treatment with a novel IAP antagonist, AT-IAP, decreased basal and TNF-α-induced cIAP-1 expression in CaP cells, switched TNF-α signaling from pro-survival to pro-apoptotic and increased radiation sensitivity of CaP cells in co-culture with THP-1 cells. We conclude that targeting cIAP-1 can overcome apoptosis resistance of CaP cells and is an ideal approach to exploit high TNF-α signals within the TAM-rich microenvironment of PTEN-deficient CaP cells to enhance response to radiotherapy.</p

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization

Accelerated Chemical Reaction Optimization Using Multi-Task Learning

Functionalization of C–H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C–H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization

Fragment-Based Drug Discovery Targeting Inhibitor of Apoptosis Proteins: Discovery of a Non-Alanine Lead Series with Dual Activity Against cIAP1 and XIAP

Inhibitor of apoptosis proteins (IAPs) are important regulators of apoptosis and pro-survival signaling pathways whose deregulation is often associated with tumor genesis and tumor growth. IAPs have been proposed as targets for anticancer therapy, and a number of peptidomimetic IAP antagonists have entered clinical trials. Using our fragment-based screening approach, we identified nonpeptidic fragments binding with millimolar affinities to both cellular inhibitor of apoptosis protein 1 (cIAP1) and X-linked inhibitor of apoptosis protein (XIAP). Structure-based hit optimization together with an analysis of protein–ligand electrostatic potential complementarity allowed us to significantly increase binding affinity of the starting hits. Subsequent optimization gave a potent nonalanine IAP antagonist structurally distinct from all IAP antagonists previously reported. The lead compound had activity in cell-based assays and in a mouse xenograft efficacy model and represents a highly promising start point for further optimization