Search

[Image: see text] Mutant isocitrate dehydrogenase 1 (IDH1) has been identified as an attractive oncology target for which >70% of grade II and III gliomas and ∼10% of acute myeloid leukemia (AML) harbor somatic IDH1 mutations. These mutations confer a neomorphic gain of function, leading to the production of the oncometabolite (R)-2-hydroxyglutarate (2-HG). We identified and developed a potent, selective, and orally bioavailable brain-penetrant tricyclic diazepine scaffold that inhibits mutant IDH1. During the course of in vitro metabolism studies, GSH-adduct metabolites were observed. The hypothesis for GSH-adduct formation was driven by the electron-rich nature of the tricyclic core. Herein, we describe our efforts to reduce the electron-rich nature of the core. Ultimately, a strategy focused on core modifications to block metabolic hot spots coupled with substitution pattern changes (C8 N → C linked) led to the identification of new tricyclic analogues with minimal GSH-adduct formation across species while maintaining an overall balanced profile.

Increasing the efficiency of the drug discovery process is a challenge faced by drug hunters everywhere. One strategy medicinal chemists employ to meet this challenge is learning from knowledge sources within and beyond their organization. In this Perspective, we discuss the evolution of mechanisms for medicinal chemistry knowledge capture and sharing at MerckCo. over the past 15 years. We describe our approach to knowledge management and report on the multiple enduring and complementary teams and initiatives we have created to capture and share knowledge within a geographically diverse medicinal chemistry community. In addition, this Perspective will share the benefits we have observed and also reflect on what has allowed our efforts to be both successful and sustainable.

The clinical success of anti-IL-17 monoclonal antibodies (i.e., Cosentyx and Taltz) has validated Th17 pathway modulation for the treatment of autoimmune diseases. The nuclear hormone receptor RORγt is a master regulator of Th17 cells and affects the production of a host of cytokines, including IL-17A, IL-17F, IL-22, IL-26, and GM-CSF. Substantial interest has been spurred across both academia and industry to seek small molecules suitable for RORγt inhibition. A variety of RORγt inhibitors have been reported in the past few years, the majority of which are orthosteric binders. Here we disclose the discovery and optimization of a class of inhibitors, which bind differently to an allosteric binding pocket. Starting from a weakly active hit 1, a tool compound 14 was quickly identified that demonstrated superior potency, selectivity, and off-target profile. Further optimization focused on improving metabolic stability. Replacing the benzoic acid moiety with piperidinyl carboxylate, modifying the 4-aza-indazole core in 14 to 4-F-indazole, and incorporating a key hydroxyl group led to the discovery of 25, which possesses exquisite potency and selectivity, as well as an improved pharmacokinetic profile suitable for oral dosing.

The leucine-rich repeat kinase 2 (LRRK2) protein has been genetically and functionally linked to Parkinson's disease (PD), a disabling and progressive neurodegenerative disorder whose current therapies are limited in scope and efficacy. In this report, we describe a rigorous hit-to-lead optimization campaign supported by structural enablement, which culminated in the discovery of brain-penetrant, candidate-quality molecules as represented by compounds

The discovery of potent, kinome selective, brain penetrant LRRK2 inhibitors is the focus of extensive research seeking new, disease-modifying treatments for Parkinson's disease (PD). Herein, we describe the discovery and evolution of a picolinamide-derived lead series. Our initial optimization efforts aimed at improving the potency and CLK2 off-target selectivity of compound 1 by modifying the heteroaryl C-H hinge and linker regions. This resulted in compound 12 which advanced deep into our research operating plan (ROP) before heteroaryl aniline metabolite 14 was characterized as Ames mutagenic, halting its progression. Strategic modifications to our ROP were made to enable early de-risking of putative aniline metabolites or hydrolysis products for mutagenicity in Ames. This led to the discovery of 3,5-diaminopyridine 15 and 4,6-diaminopyrimidine 16 as low risk for mutagenicity (defined by a 3-strain Ames negative result). Analysis of key matched molecular pairs 17 and 18 led to the prioritization of the 3,5-diaminopyridine sub-series for further optimization due to enhanced rodent brain penetration. These efforts culminated in the discovery of ethyl trifluoromethyl pyrazole 23 with excellent LRRK2 potency and expanded selectivity versus off-target CLK2.

Pathway activating mutations of the transcription factor NRF2 and its negative regulator KEAP1 are strongly correlative with poor clinical outcome with pemetrexed/carbo(cis)platin/pembrolizumab (PCP) chemo-immunotherapy in lung cancer. Despite the strong genetic support and therapeutic potential for a NRF2 transcriptional inhibitor, currently there are no known direct inhibitors of the NRF2 protein or its complexes with MAF and/or DNA. Herein we describe the design of a novel and high-confidence homology model to guide a medicinal chemistry effort that resulted in the discovery of a series of peptides that demonstrate high affinity, selective binding to the Antioxidant Response Element (ARE) DNA and thereby displace NRF2-MAFG from its promoter, which is an inhibitory mechanism that to our knowledge has not been previously described. In addition to their activity in electrophoretic mobility shift (EMSA) and TR-FRET-based assays, we show significant dose-dependent ternary complex disruption of NRF2-MAFG binding to DNA by SPR, as well as cellular target engagement by thermal destabilization of HiBiT-tagged NRF2 in the NCI–H1944 NSCLC cell line upon digitonin permeabilization, and SAR studies leading to improved cellular stability. We report the characterization and unique profile of lead peptide 18, which we believe to be a useful in vitro tool to probe NRF2 biology in cancer cell lines and models, while also serving as an excellent starting point for additional in vivo optimization toward inhibition of NRF2-driven transcription to address a significant unmet medical need in non-small cell lung cancer (NSCLC).

The Hippo pathway is an evolutionary conserved signaling network that regulates essential processes such as organ size, cell proliferation, migration, stemness and apoptosis. Alterations in this pathway are commonly found in solid tumors and can lead to hyperproliferation, resistance to chemotherapy, compensation for mKRAS and tumor immune evasion. As the terminal effectors of the Hippo pathway, the transcriptional coactivators YAP1/TAZ and the transcription factors TEAD1–4 present exciting opportunities to pharmacologically modulate the Hippo biology in cancer settings, inflammation and regenerative medicine. This review will provide an overview of the progress and current strategies to directly and indirectly target the YAP1/TAZ protein–protein interaction (PPI) with TEAD1–4 across multiple modalities, with focus on recent small molecules able to selectively bind to TEAD, block its autopalmitoylation and inhibit YAP1/TAZ–TEAD-dependent transcription in cancer.

[Image: see text] The clinical success of anti-IL-17 monoclonal antibodies (i.e., Cosentyx and Taltz) has validated Th17 pathway modulation for the treatment of autoimmune diseases. The nuclear hormone receptor RORγt is a master regulator of Th17 cells and affects the production of a host of cytokines, including IL-17A, IL-17F, IL-22, IL-26, and GM-CSF. Substantial interest has been spurred across both academia and industry to seek small molecules suitable for RORγt inhibition. A variety of RORγt inhibitors have been reported in the past few years, the majority of which are orthosteric binders. Here we disclose the discovery and optimization of a class of inhibitors, which bind differently to an allosteric binding pocket. Starting from a weakly active hit 1, a tool compound 14 was quickly identified that demonstrated superior potency, selectivity, and off-target profile. Further optimization focused on improving metabolic stability. Replacing the benzoic acid moiety with piperidinyl carboxylate, modifying the 4-aza-indazole core in 14 to 4-F-indazole, and incorporating a key hydroxyl group led to the discovery of 25, which possesses exquisite potency and selectivity, as well as an improved pharmacokinetic profile suitable for oral dosing.

The excessive energy consumption from the mining industry are currently receiving international attention. A promising method able to enhance significantly the comminution process efficiency worldwide is by using electric pulse fragmentation treatment. However, to insure a minimum energy consumption in real scale operation, an online process monitoring is of utmost importance. This work presents an in situ and real-time monitoring method by combining acoustic emission sensor and advanced machine learning algorithms. The proposed method was developed on a gold-copper ore in well-controlled single stone experiments and in semi-continuous process, reproducing a real industrial environment. In single stone experiment, the pulse energies was varied from 200 to 750 J leading to three weakening behaviours; no discharge, surface discharge and fragmentation. Acoustic signals for these categories have been decomposed with wavelet packets, and sub-band energies have been chosen as features. Then, only the most informative features were selected via standard linear principal component analysis. Finally, the classification was performed via a traditional support vector machine. In the semi-continuous experiments, an unsupervised learning method was used for classification task based on Laplacian support vector machine. Results for single stone tests showed accuracy above 90% for the three categories. For semi-continuous tests, we demonstrated that the unsupervised classification can be applied efficiently to estimate the amount of weakening of the passed through ore. We are very confident that the proposed method can be easily industrialised to monitor in situ and in real-time the electric discharge process within a comminution operation.

The mammalian Janus Kinases (JAK1, JAK2, JAK3 and TYK2) are intracellular, non-receptor tyrosine kinases whose activities have been associated in the literature and the clinic with a variety of hyperproliferative diseases and immunological disorders. At the onset of the program, it was hypothesized that a JAK1 selective compound over JAK2 could lead to an improved therapeutic index relative to marketed non-selective JAK inhibitors by avoiding the clinical AEs, such as anemia, presumably associated with JAK2 inhibition. During the course of the JAK1 program, a number of diverse chemical scaffolds were identified from both uHTS campaigns and de novo scaffold design. As part of this effort, a (benz)imidazole scaffold evolved via a scaffold-hopping exercise from a mature chemical series. Concurrent crystallography-driven exploration of the ribose pocket and the solvent front led to analogs with optimized kinome and JAK1 selectivities over the JAK2 isoform by targeting several residues unique to JAK1, such as Arg-879 and Glu-966.

An effective oxidative protocol for the liberation of ketones from SAMP hydrazones employing peroxyselenous acid under aqueous buffered conditions (pH 7) has been developed. The procedure proceeds without epimerization of adjacent stereocenters or dehydration, respectively, in representative SAMP alkylation and aldol reaction adducts.

An effective oxidative protocol for the liberation of ketones from SAMP hydrazones employing peroxyselenous acid under aqueous buffered conditions (pH 7) has been developed. The procedure proceeds without epimerization of adjacent stereocenters or dehydration, respectively, in representative SAMP alkylation and aldol reaction adducts.

4,5-Diacetamidoacridine-9(10H)-one was prepared, and its interactions with halide and benzoate anions were studied using a combination of NMR, fluorescence, and isothermal titration calorimetry experiments. Whereas chloride and bromide exhibited simple association, both fluoride and benzoate exhibited initial entropy-driven association followed by an enthalpically favorable deprotonation of the receptor by a second equivalent of the anion.

The purpose of the dissertation is to develop and experiment testing methods for creating recognition systems formation of images combining transfer learning processing models with the accuracy and flexibility of convolutional neurons networks. An in-depth review and analysis of the issues related to the influence of the transfer function used by the neural network on the data classification process was made. Based on this study, their advantages and disadvantages are identified and the Swish transfer function is selected. The architectures of the existing neural networks and the special ones for image processing are analysed. Three algorithms using a neural network have been trained and studied to classify severely damaged vehicles. The convolutional neural network was chosen for the needs of the dissertation. An algorithm for detecting crashed cars in road accidents has been proposed. The algorithm is implemented on a local Linux based machine to perform neural network operations. This algorithm can be refined and used in transport management systems. Целта на дисертацията е да се разработят и експериментират методи за тестване за създаване на системи за разпознаване, формиране на изображения, съчетаващи модели за обработка на преносно обучение с точността и гъвкавостта на конволюционните невронни мрежи. Направен е задълбочен обзор и анализ на въпросите свързани с влиянието на предавателната функция използвана от неврона мрежа върху процеса на класификация на данни. Въз основа на това изследване са посочени предимствата и недостатъците им и е избрана предавателната функция Swish. Анализирани са архитектурите на съществуващите неврони мрежи и по специално тези за обработка на изображения. Обучени и изследвани са три алгоритъма използващи неврона мрежа с цел класификация на тежко повредени превозни средства. За нуждите на дисертационният труд е избрана конволюционната неврона мрежа. Предложен е алгоритъм за откриване на катастрофирали автомобили при пътни инциденти. Алгоритъма е реализиран на локална Linux базирана машина за извършване на операции с неврона мрежа. Този алгоритъм може да се доработи и да се използва в системи за управление на транспорта.