Decoding kinase-adverse event associations for small molecule kinase inhibitors

Design, synthesis, and X-ray structural studies of a series of highly potent, selective, and drug-like G protein-coupled receptor kinase 5 inhibitors

G protein-coupled receptor kinase 5 (GRK5) has emerged as a potential drug development target against heart failure and cancer. A close homolog, GRK6 represents a therapeutic target for multiple myeloma. We have rationally designed a series of highly selective, potent, noncovalent, and drug-like GRK5 inhibitors. Several inhibitors exhibited low nanomolar GRK5 inhibition and high selectivity over GRK2, and, surprisingly, some were selective for GRK6. We determined high-resolution X-ray crystal structures of several inhibitors in complex with GRK5, which provide molecular insights into the ligand-binding site interactions responsible for GRK5 selectivity and potency.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Can Machine Learning Overcome the 95% Failure Rate and Reality that Only 30% of Approved Cancer Drugs Meaningfully Extend Patient Survival?

Despite implementing hundreds of strategies, cancer drug development suffers from a 95% failure rate over 30 years, with only 30% of approved cancer drugs extending patient survival beyond 2.5 months. Adding more criteria without eliminating nonessential ones is impractical and may fall into the "survivorship bias" trap. Machine learning (ML) models may enhance efficiency by saving time and cost. Yet, they may not improve success rate without identifying the root causes of failure. We propose a "STAR-guided ML system" (structure-tissue/cell selectivity-activity relationship) to enhance success rate and efficiency by addressing three overlooked interdependent factors: potency/specificity to the on/off-targets determining efficacy in tumors at clinical doses, on/off-target-driven tissue/cell selectivity influencing adverse effects in the normal organs at clinical doses, and optimal clinical doses balancing efficacy/safety as determined by potency/specificity and tissue/cell selectivity. STAR-guided ML models can directly predict clinical dose/efficacy/safety from five features to design/select the best drugs, enhancing success and efficiency of cancer drug development.

AI for targeted polypharmacology: The next frontier in drug discovery

In drug discovery, targeted polypharmacology, i.e., targeting multiple molecular targets with a single drug, is redefining therapeutic design to address complex diseases. Pre-selected pharmacological profiles, as exemplified in kinase drugs, promise enhanced efficacy and reduced toxicity. Historically, many of such drugs were discovered serendipitously, limiting predictability and efficacy, but currently artificial intelligence (AI) offers a transformative solution. Machine learning and deep learning techniques enable modeling protein structures, generating novel compounds, and decoding their polypharmacological effects, opening an avenue for more systematic and predictive multi-target drug design. This review explores the use of AI in identifying synergistic co-targets and delineating them from anti-targets that lead to adverse effects, and then discusses advances in AI-enabled docking, generative chemistry, and proteochemometric modeling of proteome-wide compound interactions, in the context of polypharmacology. We also provide insights into challenges ahead.

Development of a new class of potent and highly selective G protein-coupled receptor kinase 5 inhibitors and structural insight from crystal structures of inhibitor complexes

G protein-coupled receptor kinase 5 (GRK5) is an important drug development target for heart failure, cardiac hypertrophy, and cancer. We have designed and developed a new class of highly selective, potent, and non-covalent GRK5 inhibitors. One of the inhibitors displayed GRK5 IC50 value of 10 nM and exhibited >100,000-fold selectivity over GRK2. The X-ray structure of a ketoamide-derived inhibitor-bound GRK5 showed the formation of a hemithioketal intermediate with active site Cys474 in the GRK5 active site and provided new insights into the ligand-binding site interactions responsible for high selectivity. The current studies serve as an important guide to therapeutic GRK5 inhibitor drug development.

The latest perspectives of small molecules FMS kinase inhibitors

FMS kinase is a type III tyrosine kinase receptor that plays a central role in the pathophysiology and management of several diseases, including a range of cancer types, inflammatory disorders, neurodegenerative disorders, and bone disorders among others. In this review, the pathophysiological pathways of FMS kinase in different diseases and the recent developments of its monoclonal antibodies and inhibitors during the last five years are discussed. The biological and biochemical features of these inhibitors, including binding interactions, structure-activity relationships (SAR), selectivity, and potencies are discussed. The focus of this article is on the compounds that are promising leads and undergoing advanced clinical investigations, as well as on those that received FDA approval. In this article, we attempt to classify the reviewed FMS inhibitors according to their core chemical structure including pyridine, pyrrolopyridine, pyrazolopyridine, quinoline, and pyrimidine derivatives.

Role of c-Src and reactive oxygen species in cardiovascular diseases

Oxidative stress, caused by the over production of oxidants or inactivity of antioxidants, can modulate the redox state of several target proteins such as tyrosine kinases, mitogen-activated protein kinases and tyrosine phosphatases. c-Src is one such non-receptor tyrosine kinase which activates NADPH oxidases (Noxs) in response to various growth factors and shear stress. Interaction between c-Src and Noxs is influenced by cell type and primary messengers such as angiotensin II, which binds to G-protein coupled receptor and activates the intracellular signaling cascade. c-Src stimulated activation of Noxs results in elevated release of intracellular and extracellular reactive oxygen species (ROS). These ROS species disturb vascular homeostasis and cause cardiac hypertrophy, coronary artery disease, atherosclerosis and hypertension. Interaction between c-Src and ROS in the pathobiology of cardiac fibrosis is hypothesized to be influenced by cell type and stimuli. c-Src and ROS have a bidirectional relationship, thus increased ROS levels due to c-Src mediated activation of Noxs can further activate c-Src by promoting the oxidation and sulfenylation of critical cysteine residues. This review highlights the role of c-Src and ROS in mediating downstream signaling pathways underlying cardiovascular diseases. Furthermore, due to the central role of c-Src in activation of various signaling proteins involved in differentiation, migration, proliferation, and cytoskeletal reorganization of vascular cells, it is presented as therapeutic target for treating cardiovascular diseases except cardiac fibrosis.

A patent review of MAPK inhibitors (2018 - present)

INTRODUCTION

The mitogen-activated protein kinase (MAPK) family consist of p38 MAP kinases, c-Jun N-terminal kinases (JNKs) and extracellular signal-regulated kinases (ERKs). They are involved in a multitude of diseases, including inflammatory, autoimmune, neurodegenerative, and metabolic diseases as well as cancer. In recent years, further developments in the field of MAPK-inhibitors have been reported, including an isoform or downstream target selective inhibition of MAPKs as well as target protein degradation approaches.

AREAS COVERED

This review summarizes newly patented MAPK-inhibitors that were claimed between 2018 and early 2023. Presented are the patents as well as their corresponding publications, the storyline of development, and clinical trials involving these compounds. This article elaborates a total of 27 patents, which were identified using established search engines.

EXPERT OPINION

Although industrial research on MAPK-inhibitors has been ongoing for more than 20 years, novel clinical trials of MAPK-inhibitors as potential drug candidates are still being conducted in the period under review. Recently reported inhibitors show an excellent selectivity profile and are even achieving selectivity between closely related isoforms. This progression offers the possibility to eliminate unwanted side effects and may finally lead to the approval of the first MAPK-inhibitor.