Fentanyl Structure as a Scaffold for Opioid/Non-Opioid Multitarget Analgesics

Multitarget ligands that comprise opioid/nonopioid pharmacophores for pain management: Current state of the science

Chronic pain, which affects more than one-third of the world's population, represents one of the greatest medical challenges of the 21st century, yet its effective management remains sub-optimal. The 'gold standard' for the treatment of moderate to severe pain consists of opioid ligands, such as morphine and fentanyl, that target the µ-opioid receptor (MOP). Paradoxically, these opioids also cause serious side effects, including constipation, respiratory depression, tolerance, and addiction. In addition, the development of opioid-use disorders, such as opioid diversion, misuse, and abuse, has led to the current opioid crisis, with dramatic increases in addiction, overdoses, and ultimately deaths. As pain is a complex, multidimensional experience involving a variety of pathways and mediators, dual or multitarget ligands that can bind to more than one receptor and exert complementary analgesic effects, represent a promising avenue for pain relief. Indeed, unlike monomodal therapeutic approaches, the modulation of several endogenous nociceptive systems can often result in an additive or even synergistic effect, thereby improving the analgesic-to-side-effect ratio. Here, we provide a comprehensive overview of research efforts towards the development of dual- or multi-targeting opioid/nonopioid hybrid ligands for effective and safer pain management. We reflect on the underpinning discovery rationale by discussing the design, medicinal chemistry, and in vivo pharmacological effects of multitarget antinociceptive compounds.

BNT12, a novel hybrid peptide of opioid and neurotensin pharmacophores, produces potent central antinociception with limited side effects

The development of multitarget opioid drugs has emerged as an attractive approach for innovative pain management with reduced side effects. In the present study, a novel hybrid peptide BNT12 containing the opioid and neurotensin (NT)-like fragments was synthesized and pharmacologically characterized. In acute radiant heat paw withdrawal test, intracerebroventricular (i.c.v.) administration of BNT12 produced potent antinociception in mice. The central antinociceptive activity of BNT12 was mainly mediated by μ-, δ-opioid receptor, neurotensin receptor type 1 (NTSR1) and 2 (NTSR2), supporting a multifunctional agonism of BNT12 in the functional assays. BNT12 also exhibited significant antinociceptive effects in spared nerve injury (SNI)-neuropathic pain, complete Freund's adjuvant (CFA)-induced inflammatory pain, acetic acid-induced visceral and formalin-induced pain after i.c.v. administration. Furthermore, BNT12 exhibited substantial reduction of acute antinociceptive tolerance, shifted the dose-response curve to the right by only 1.3-fold. It is noteworthy that BNT12 showed insignificant chronic antinociceptive tolerance at the supraspinal level. In addition, BNT12 exhibited reduced or no opioid-like side effects on conditioned place preference (CPP) response, naloxone-precipitated withdrawal response, acute hyperlocomotion, motor coordination, gastrointestinal transit, and cardiovascular responses. The present investigation demonstrated that the novel hybrid peptide BNT12 might serve as a promising analgesic candidate with limited opioid-like side effects.

Novel Dual-Acting Hybrids Targeting Type-2 Cannabinoid Receptors and Cholinesterase Activity Show Neuroprotective Effects In Vitro and Amelioration of Cognitive Impairment In Vivo

Alzheimer's disease (AD) is a neurodegenerative form of dementia characterized by the loss of synapses and a progressive decline in cognitive abilities. Among current treatments for AD, acetylcholinesterase (AChE) inhibitors have efficacy limited to symptom relief, with significant side effects and poor compliance. Pharmacological agents that modulate the activity of type-2 cannabinoid receptors (CB2R) of the endocannabinoid system by activating or blocking them have also been shown to be effective against neuroinflammation. Herein, we describe the design, synthesis, and pharmacological effects in vitro and in vivo of dual-acting compounds that inhibit AChE and butyrylcholinesterase (BChE) and target CB2R. Within the investigated series, compound 4g proved to be the most promising. It achieved IC50 values in the low micromolar to submicromolar range against both human cholinesterase isoforms while antagonizing CB2R with Ki of 31 nM. Interestingly, 4g showed neuroprotective effects on the SH-SY5Y cell line thanks to its ability to prevent oxidative stress-induced cell toxicity and reverse scopolamine-induced amnesia in the Y-maze forced alternation test in vivo.

IUPHAR Review - Bivalent and bifunctional opioid receptor ligands as novel analgesics

Though efficacious in managing chronic, severe pain, opioid analgesics are accompanied by significant adverse effects including constipation, tolerance, dependence, and respiratory depression. The life-threatening risks associated with µ opioid receptor agonist-based analgesics challenges their use in clinic. A rational approach to combatting these adverse effects is to develop agents that incorporate activity at a second pharmacologic target in addition to µ opioid receptor activation. The promise of such bivalent or bifunctional ligands is the development of an analgesic with an improved side effect profile. In this review, we highlight ongoing efforts in the development of bivalent and bifunctional analgesics that combine µ agonism with efficacy at κ and δ opioid receptors, the nociceptin opioid peptide (NOP) receptor, σ receptors, and cannabinoid receptors. Several examples of bifunctional analgesics in preclinical and clinical development are highlighted, as are strategies being employed toward the rational design of novel agents.

Endogenous opiates and behavior: 2022

This paper is the forty-fifth consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2022 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug abuse and alcohol (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions

Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.