Flupirtine drug-induced liver injury in a patient developing acute liver failure

Disease-modifying rdHSV-CA8* non-opioid analgesic gene therapy treats chronic osteoarthritis pain by activating Kv7 voltage-gated potassium channels

Chronic pain is common in our population, and most of these patients are inadequately treated, making the development of safer analgesics a high priority. Knee osteoarthritis (OA) is a primary cause of chronic pain and disability worldwide, and lower extremity OA is a major contributor to loss of quality-adjusted life-years. In this study we tested the hypothesis that a novel JDNI8 replication-defective herpes simplex-1 viral vector (rdHSV) incorporating a modified carbonic anhydrase-8 transgene (CA8*) produces analgesia and treats monoiodoacetate-induced (MIA) chronic knee pain due to OA. We observed transduction of lumbar DRG sensory neurons with these viral constructs (vHCA8*) (~40% of advillin-positive cells and ~ 50% of TrkA-positive cells colocalized with V5-positive cells) using the intra-articular (IA) knee joint (KJ) route of administration. vHCA8* inhibited chronic mechanical OA knee pain induced by MIA was dose- and time-dependent. Mechanical thresholds returned to Baseline by D17 after IA KJ vHCA8* treatment, and exceeded Baseline (analgesia) through D65, whereas negative controls failed to reach Baseline responses. Weight-bearing and automated voluntary wheel running were improved by vHCA8*, but not negative controls. Kv7 voltage-gated potassium channel-specific inhibitor XE-991 reversed vHCA8*-induced analgesia. Using IHC, IA KJ of vHCA8* activated DRG Kv7 channels via dephosphorylation, but negative controls failed to impact Kv7 channels. XE-991 stimulated Kv7.2-7.5 and Kv7.3 phosphorylation using western blotting of differentiated SH-SY5Y cells, which was inhibited by vHCA8* but not by negative controls. The observed prolonged dose-dependent therapeutic effects of IA KJ administration of vHCA8* on MIA-induced chronic KJ pain due to OA is consistent with the specific activation of Kv7 channels in small DRG sensory neurons. Together, these data demonstrate for the first-time local IA KJ administration of vHCA8* produces opioid-independent analgesia in this MIA-induced OA chronic pain model, supporting further therapeutic development.

rdHSV-CA8 non-opioid analgesic gene therapy decreases somatosensory neuronal excitability by activating Kv7 voltage-gated potassium channels

Chronic pain is common and inadequately treated, making the development of safe and effective analgesics a high priority. Our previous data indicate that carbonic anhydrase-8 (CA8) expression in dorsal root ganglia (DRG) mediates analgesia via inhibition of neuronal ER inositol trisphosphate receptor-1 (ITPR1) via subsequent decrease in ER calcium release and reduction of cytoplasmic free calcium, essential to the regulation of neuronal excitability. This study tested the hypothesis that novel JDNI8 replication-defective herpes simplex-1 viral vectors (rdHSV) carrying a CA8 transgene (vHCA8) reduce primary afferent neuronal excitability. Whole-cell current clamp recordings in small DRG neurons showed that vHCA8 transduction caused prolongation of their afterhyperpolarization (AHP), an essential regulator of neuronal excitability. This AHP prolongation was completely reversed by the specific Kv7 channel inhibitor XE-991. Voltage clamp recordings indicate an effect via Kv7 channels in vHCA8-infected small DRG neurons. These data demonstrate for the first time that vHCA8 produces Kv7 channel activation, which decreases neuronal excitability in nociceptors. This suppression of excitability may translate in vivo as non-opioid dependent behavioral- or clinical analgesia, if proven behaviorally and clinically.

Remodelling structure-based drug design using machine learning

To keep up with the pace of rapid discoveries in biomedicine, a plethora of research endeavors had been directed toward Rational Drug Development that slowly gave way to Structure-Based Drug Design (SBDD). In the past few decades, SBDD played a stupendous role in identification of novel drug-like molecules that are capable of altering the structures and/or functions of the target macromolecules involved in different disease pathways and networks. Unfortunately, post-delivery drug failures due to adverse drug interactions have constrained the use of SBDD in biomedical applications. However, recent technological advancements, along with parallel surge in clinical research have led to the concomitant establishment of other powerful computational techniques such as Artificial Intelligence (AI) and Machine Learning (ML). These leading-edge tools with the ability to successfully predict side-effects of a wide range of drugs have eventually taken over the field of drug design. ML, a subset of AI, is a robust computational tool that is capable of data analysis and analytical model building with minimal human intervention. It is based on powerful algorithms that use huge sets of 'training data' as inputs to predict new output values, which improve iteratively through experience. In this review, along with a brief discussion on the evolution of the drug discovery process, we have focused on the methodologies pertaining to the technological advancements of machine learning. This review, with specific examples, also emphasises the tremendous contributions of ML in the field of biomedicine, while exploring possibilities for future developments.