Library of opioid binding patterns in correlation with their adverse side effects

Opioids represent crucial drugs in the treatment of moderate and severe pain in diseases like cancer and in traumatic injury. They are, however, associated with severe adverse side effects, notably with respiratory depression that can lead to death. The high addiction potential of opioids relates to their frequent abuse, which has led to the opioid overdose crisis resulting in the U.S. in over 70,000 deaths in the year 2020 alone. Therefore, there is a strong need for novel opioids with milder side effects. To this end, the proposed research project will employ complementary state-of-the-art computational and experimental techniques to correlate opioid structural and binding patterns to their pharmacological activities and adverse side effects. Such knowledge is crucial for the future design of safer opioids. Several computational methods will be used to discern the biological phenomena leading to opioids activating the μ-opioid receptor (MOR), which forms the most important receptor sub-type concerning both analgesia and adverse side effects. We will use the in-house developed ProBiS-Dock algorithm to identify opioid binding poses. Molecular dynamics (MD) simulations will be used to identify dynamic binding patterns, thus obtaining information of how non-covalent interactions between opioids and MOR are formed and broken throughout time. Recently, a potentially safer opioid was developed by our collaborators - a fentanyl derivative NFEPP. Because opioids must be positively charged to be active, the low pKa of NFEPP compared to other opioids allows for selective activation of MOR only at low pH, which is characteristic for inflamed tissues in the periphery. NFEPP is therefore active at the source of pain signaling, meanwhile bypassing the activation in the central nervous system (CNS). Therefore, we will also perform MD simulations in systems characteristic for acidic pH environments. MD simulations will also be used to perform rigorous free energy calculations of opioid binding, which are directly comparable with experimentally determined inhibition constants. Due to our previously developed automatic procedure for obtaining force-field parameters for opioids, we are in a unique position to perform MD simulations with unprecedented accuracy. The dynamic binding patterns obtained with MD simulations will be clustered to identify structural and binding features that lead to similar pharmacological activities and adverse side effects. Moreover, we will employ high accuracy quantum-mechanical simulations in explicit water to address the importance of torsional strain in opioids. These will provide low-energy conformations of flexible opioids, which will serve as comparative structures to validate the structures obtained by molecular docking and MD, as low-energy conformations often correspond to those complementing the receptor. Whereas, detailed pharmacological data and adverse side effect profiles for established opioids can be obtained from the scientific literature, this is not true for newly developed compounds. To establish the appropriate pharmacological profiles to which our data could be correlated, we will perform in vitro testing of opioid binding as well as measurements of the subsequent G protein activation. To obtain a wholesome pharmacological profile, in vivo testing will be performed implementing rat models of pain and analgesia. The ultimate goal is to establish a standardized procedure which would facilitate the correlation of any opioid structure to its pharmacological profile, as this will enable medicinal chemists to concentrate on structures that exhibit more favorable safety profiles. As novel drug design represents an interdisciplinary process, our results will be shared with the international scientific community over a freely available modern web application, which will contain an interactive library of opioid binding features and their correlations with pharmacological activities.