CHARMM Force-Field Parameters for Morphine, Heroin, and Oliceridine, and Conformational Dynamics of Opioid Drugs

Is personal physiology-based rapid prediction digital twin for minimal effective fentanyl dose better than standard practice: a pilot study protocol

INTRODUCTION

Patients with advanced cancer frequently suffer from chronic, severe disabling pain. Opioids such as morphine and fentanyl are commonly used to manage this pain. Transdermal drug delivery systems are important technologies for administering drugs in a non-invasive, continuous and controlled manner. Due to the narrow therapeutic range of fentanyl, individualised dosing is essential to avoid underdosing or overdosing. Standard clinical calculation tools for opioid rotation however do not include important patient characteristics that account for interindividual variability of opioid pharmacology.

METHODS AND ANALYSIS

We developed a clinical protocol to optimise individual fentanyl dosing in patients with advanced cancer switching from oral or intravenous opioids to transdermal fentanyl by using a physics-based digital twin (DT) that is fed by important clinical and physiological parameters. Individual tailoring of transdermal fentanyl therapy is an approach with the potential for personalised and effective care with an improved benefit-risk ratio. However, clinical validation of physics-based digital twins (PBDT) dosing is crucial to proving clinical benefit.Therapeutic drug monitoring will allow to validate the accuracy of PBDT predictions. Additional monitoring for breathing dynamics, sequential pain levels and fentanyl-related adverse events will contribute to evaluating the performance of PBDT-based dosing of transdermal fentanyl. The primary objective of the study is to develop an experimental protocol to validate DT-guided fentanyl dosing in patients with advanced cancer. This clinical study will bring individualised opioid dosing closer to clinical practice.

ETHICS AND DISSEMINATION

Study documents have been approved by the responsible Ethics Committee and study initiation is planned for late summer 2024. Data will be shared with the scientific community no more than 1 year following completion of the study and data assembly.

Surfactant-Polymer Complexation and Competition on Drug Nanocrystal Surfaces Control Crystallinity

Nanosizing drug crystals has emerged as a successful approach to enabling oral bioavailability, as increasing drug crystal surface area improves dissolution kinetics and effective solubility. Recently, bottom-up methods have been developed to directly assemble nanosized crystals by leveraging polymer and surfactant excipients during crystallization to control crystal size, morphology, and structure. However, while significant research has investigated how polymers and other single additives inhibit or promote crystallization in pharmaceutical systems, there is little work studying the mechanistic interactions of multiple excipients on drug crystal structure and the extent of crystallinity, which can influence formulation performance. This study explores how the structure and crystallinity of a model hydrophobic drug crystal, fenofibrate, change as a result of competitive interfacial chemisorption between common nonionic surfactants (polysorbate 80 and sorbitan monooleate) and a surface-active polymer excipient (methylcellulose). Classical molecular dynamics simulations highlight how key intermolecular interactions, including surfactant-polymer complexation and surfactant screening of the crystal surface, modify the resulting crystal structure. In parallel, experiments generating drug nanocrystals in hydrogel thin films validate that drug crystallinity increases with an increasing weight fraction of surfactant. Simulation results reveal a connection between accelerated dynamics in the bulk crystal and the experimentally measured extent of crystallinity. To our knowledge, these are the first simulations that directly characterize structural changes in a drug crystal as a result of excipient surface composition and relate the experimental extent of crystallinity to structural changes in the molecular crystal. Our approach provides a mechanistic understanding of crystallinity in nanocrystallization, which can expand the range of orally deliverable small molecule therapies.

ERNEST COST action overview on the (patho)physiology of GPCRs and orphan GPCRs in the nervous system

G protein-coupled receptors (GPCRs) are a large family of cell surface receptors that play a critical role in nervous system function by transmitting signals between cells and their environment. They are involved in many, if not all, nervous system processes, and their dysfunction has been linked to various neurological disorders representing important drug targets. This overview emphasises the GPCRs of the nervous system, which are the research focus of the members of ERNEST COST action (CA18133) working group 'Biological roles of signal transduction'. First, the (patho)physiological role of the nervous system GPCRs in the modulation of synapse function is discussed. We then debate the (patho)physiology and pharmacology of opioid, acetylcholine, chemokine, melatonin and adhesion GPCRs in the nervous system. Finally, we address the orphan GPCRs, their implication in the nervous system function and disease, and the challenges that need to be addressed to deorphanize them.

Implementing physics-based digital patient twins to tailor the switch of oral morphine to transdermal fentanyl patches based on patient physiology

Fentanyl transdermal patches are widely implemented for cancer-induced pain treatment due to the high potency of fentanyl and gradual drug release. However, transdermal fentanyl up-titration for opioid-naïve patients is difficult, which is why opioid treatment is often started with oral/iv morphine. Based on the daily dose of morphine, the initial dose of the fentanyl patch is decided upon. After reaching a stable level of pain, the switch is made from oral/iv morphine to transdermal fentanyl. There are standard calculation tools for transferring from oral/iv morphine to transdermal fentanyl, which is the same for all patients. By considering the variations in the physiology of the patients, a unique switching strategy cannot meet the needs of different patients. This study explores the outcome in terms of pain relief and minute ventilation during opioid therapy. For this, we used physics-based simulations on a virtually-generated population of patients, and we applied the same therapy to all patients. We could show that patients' physiology, such as gender, age, and weight, greatly impact the outcome of the therapy; as such, the correlation coefficient between pain intensity and age is 0.89, and the correlation coefficient between patient's weight and maximum plasma concentration of morphine and fentanyl is -0.98 and -0.97. Additionally, a different combination of the duration of overlap between morphine and fentanyl therapy with different doses of fentanyl was considered for the virtual patients to find the best opioid-switching strategy for each patient. We explored the impact of combining physiological features to determine the best-suited strategy for virtual patients. Our findings suggest that tailoring morphine and fentanyl therapy only based on a limited number of features is insufficient, and increasing the number of impactful physiological features positively influences the outcome of the therapy.

An overview of the SAMPL8 host-guest binding challenge

The SAMPL series of challenges aim to focus the community on specific modeling challenges, while testing and hopefully driving progress of computational methods to help guide pharmaceutical drug discovery. In this study, we report on the results of the SAMPL8 host–guest blind challenge for predicting absolute binding affinities. SAMPL8 focused on two host–guest datasets, one involving the cucurbituril CB8 (with a series of common drugs of abuse) and another involving two different Gibb deep-cavity cavitands. The latter dataset involved a previously featured deep cavity cavitand (TEMOA) as well as a new variant (TEETOA), both binding to a series of relatively rigid fragment-like guests. Challenge participants employed a reasonably wide variety of methods, though many of these were based on molecular simulations, and predictive accuracy was mixed. As in some previous SAMPL iterations (SAMPL6 and SAMPL7), we found that one approach to achieve greater accuracy was to apply empirical corrections to the binding free energy predictions, taking advantage of prior data on binding to these hosts. Another approach which performed well was a hybrid MD-based approach with reweighting to a force matched QM potential. In the cavitand challenge, an alchemical method using the AMOEBA-polarizable force field achieved the best success with RMSE less than 1 kcal/mol, while another alchemical approach (ATM/GAFF2-AM1BCC/TIP3P/HREM) had RMSE less than 1.75 kcal/mol. The work discussed here also highlights several important lessons; for example, retrospective studies of reference calculations demonstrate the sensitivity of predicted binding free energies to ethyl group sampling and/or guest starting pose, providing guidance to help improve future studies on these systems.

Recent Molecular Insights into Agonist-specific Binding to the Mu-Opioid Receptor

Opioid agonists produce their analgesic effects primarily by acting at the µ-opioid receptor (µOR). µOR agonists with different efficacies exert diverse molecular changes in the µOR which dictate the faith of the receptor’s signaling pathway and possibly it’s the degree of desensitization. Since the development of the active conformations of the µOR, growing data have been published in relation to ligand-specific changes in µOR activation. In this regard, this review summarizes recent data regarding the most studied opioid agonists in in silico µOR activation, including how these ligands are recognized by the µOR, how their binding signal is transmitted toward the intracellular parts of the µOR, and finally, what type of large-scale movements do these changes trigger in the µOR’s domains.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

A comprehensive evaluation of the potential binding poses of fentanyl and its analogs at the µ-opioid receptor

Fentanyl and its analogs are selective agonists of the µ-opioid receptor (MOR). Among novel synthetic opioids (NSOs), they dominate the recreational drug market and are the main culprits for the opioid crisis, which has been exacerbated by the COVID-19 pandemic. By taking advantage of the crystal structures of the MOR, several groups have investigated the binding mechanism of fentanyl, but have not reached a consensus, in terms of both the binding orientation and the fentanyl conformation. Thus, the binding mechanism of fentanyl at the MOR remains an unsolved and challenging question. Here, we carried out a systematic computational study to investigate the preferred fentanyl conformations, and how these conformations are being accommodated in the MOR binding pocket. We characterized the free energy landscape of fentanyl conformations with metadynamics simulations, and compared and evaluated several possible fentanyl binding conditions in the MOR with long-timescale molecular dynamics simulations. Our results indicate that the most preferred binding pose in the MOR binding pocket corresponds well with the global minimum on the energy landscape of fentanyl in the absence of the receptor, while the energy landscape can be reconfigured by modifying the fentanyl scaffold. The interactions with the receptor may stabilize a slightly unfavored fentanyl conformation in an alternative binding pose. By extending similar investigations to fentanyl analogs, our findings establish a structure–activity relationship of fentanyl binding at the MOR. In addition to providing a structural basis to understand the potential toxicity of the emerging NSOs, such insights will contribute to developing new, safer analgesics.

Investigating the binding mechanism of topiramate with bovine serum albumin using spectroscopic and computational methods

Various spectroscopic techniques involving fluorescence spectroscopy, circular dichroism (CD), and computational approaches were used to elucidate the molecular aspects of interaction between the antiepileptic drug topiramate and the multifunctional transport protein bovine serum albumin (BSA) under physiological conditions. Topiramate quenched BSA fluorescence in a static quenching mode, according to the Stern-Volmer quenching constant (K_sv ) data derived from fluorescence spectroscopy for the topiramate-BSA complex. The binding constant was also used to calculate the binding affinity for the topiramate-BSA interaction. Fluorescence and circular dichroism experiments demonstrate that the protein's tertiary structure is affected by the microenvironmental alterations generated by topiramate binding to BSA. To establish the exact binding site, interacting residues, and interaction forces involved in the binding of topiramate to BSA, molecular modeling and simulation approaches were used. According to the MMPBSA calculations, the average binding energy between topiramate and BSA is -421.05 kJ/mol. Topiramate was discovered to have substantial interactions with BSA, changing the structural dynamic and Gibbs free energy landscape patterns.

Opioid Receptors and Protonation-Coupled Binding of Opioid Drugs

Opioid receptors are G-protein-coupled receptors (GPCRs) part of cell signaling paths of direct interest to treat pain. Pain may associate with inflamed tissue characterized by acidic pH. The potentially low pH at tissue targeted by opioid drugs in pain management could impact drug binding to the opioid receptor, because opioid drugs typically have a protonated amino group that contributes to receptor binding, and the functioning of GPCRs may involve protonation change. In this review, we discuss the relationship between structure, function, and dynamics of opioid receptors from the perspective of the usefulness of computational studies to evaluate protonation-coupled opioid-receptor interactions.

Molecular Dynamics Simulations of the Aptamer Domain of Guanidinium Ion Binding Riboswitch ykkC-III: Structural Insights into the Discrimination of Cognate and Alternate Ligands

Guanidinium ion is a toxic cellular metabolite. The ykkC-III riboswitch, an mRNA stretch, regulates the gene expression by undergoing a conformational change in response to the binding of a free guanidinium ion and thereby plays a potentially important role in alleviating guanidinium toxicity in cells. An experimental crystal structure of the guanidinium-bound aptamer domain of the riboswitch from Thermobifida Fusca revealed the overall RNA architecture and mapped the specific noncovalent interactions that stabilize the ligand within the binding pocket aptamer. However, details of how the aptamer domain discriminates the cognate ligand from its closest structurally analogous physiological metabolites (arginine and urea), and how the binding of cognate ligand arrays information from the aptamer domain to the expression platform for regulating the gene expression, are not well understood. To fill this void, we perform a cumulative of 2 μs all-atom explicit-solvent molecular dynamics (MD) simulations on the full aptamer domain, augmented with quantum-chemical calculations on the ligand-binding pocket, to compare the structural and dynamical details of the guanidinium-bound state with the arginine or urea bound states, as well as the unbound (open) state. Analysis of the ligand-binding pocket reveals that due to unfavorable interactions with the binding-pocket residues, urea cannot bind the aptamer domain and thereby cannot alter the gene expression. Although interaction of the guanidyl moiety of arginine within the binding pocket is either comparable or stronger than the guanidinium ion, additional non-native hydrogen-bonding networks, as well as differences in the dynamical details of the arginine-bound state, explain why arginine cannot transmit the information from the aptamer domain to the expression platform. Based on our simulations, we propose a mechanism of how the aptamer domain communicates with the expression platform. Overall, our work provides interesting insights into the ligand recognition by a specific class of riboswitches and may hopefully inspire future studies to further understand the gene regulation by riboswitches.