Molecular simulations reveal the impact of RAMP1 on ligand binding and dynamics of calcitonin gene-related peptide receptor (CGRPR) heterodimer

The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer

Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP's function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

Discovery of potent DNMT1 inhibitors against sickle cell disease using structural-based virtual screening, MM-GBSA and molecular dynamics simulation-based approaches

Sickle cell disease (SCD) is an autosomal recessive genetic disorder affecting millions of people worldwide. A reversible and selective DNMT1 inhibitor, GSK3482364, has been known to decrease the overall methylation activity of DNMT1, resulting in the increase of HbF levels and percentage of HbF-expressing erythrocytes in an in vitro and in vivo model. In this study, a structure-based virtual screening was done with GSK3685032, a co-crystalized ligand of DNMT1 (PDB ID: 6X9K) with an IC50 value of 0.036 μM and identified 3988 compounds from three databases (ChEMBL, PubChem and Drug Bank). Using this screening method, we identified around 15 compounds with XP docking scores greater than -8 kcal/mol. Further, prime MM-GBSA calculations have been performed and found compound SCHEMBL19716714 with the highest binding free energy of -83.31 kcal/mol. Finally, four compounds were identified based on glide energy and ΔG bind scores that have the most binding with DG7, DG19, DG20 bases and Lys1535, His1507, Trp1510, Ser1230, which were required for the target enzyme inhibition. Furthermore, molecular dynamics simulation studies of top ligands validate the stability of the docked complexes by examining root mean square deviations, root mean square fluctuations, solvent accessible surface area, and radius of gyration graphs from simulation trajectories. These findings suggest that the top four hit compounds may be capable of inhibiting DNMT1 and that additional in vitro and in vivo studies will be essential to prove the clinical effectiveness of the selected lead compounds.Communicated by Ramaswamy H. Sarma.

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

The human ether-à-go-go-related gene (hERG) channel plays a crucial role in membrane repolarization. Any disruptions in its function can lead to severe cardiovascular disorders such as long QT syndrome (LQTS), which increases the risk of serious cardiovascular problems such as tachyarrhythmia and sudden cardiac death. Drug-induced LQTS is a significant concern and has resulted in drug withdrawals from the market in the past. The main objective of this study is to pinpoint crucial heteroatoms present in ligands that initiate interactions leading to the effective blocking of the hERG channel. To achieve this aim, ligand-based quantitative structure-activity relationships (QSAR) models were constructed using extensive ligand libraries, considering the heteroatom types and numbers, and their associated hERG channel blockage pIC50 values. Machine learning-assisted QSAR models were developed to analyze the key structural components influencing compound activity. Among the various methods, the KPLS method proved to be the most efficient, allowing the construction of models based on eight distinct fingerprints. The study delved into investigating the influence of heteroatoms on the activity of hERG blockers, revealing their significant role. Furthermore, by quantifying the effect of heteroatom types and numbers on ligand activity at the hERG channel, six compound pairs were selected for molecular docking. Subsequent molecular dynamics simulations and per residue MM/GBSA calculations were performed to comprehensively analyze the interactions of the selected pair compounds.

Virtual drug repurposing study for the CGRPR identifies pentagastrin and leuprorelin as putative candidates

Calcitonin gene-related peptide receptor (CGRPR) is a heterodimer consisting of CLR and RAMP1 proteins. Activation of the CGRPR with the endogenous peptide CGRP is known to play a crucial role in migraine pathophysiology. CGRP occupies two regions in the CGRPR upon binding, namely ectodomain and transmembrane sites (sites 1 and 2, respectively). The disruption of the CGRPR heterodimer interface is one of the main strategies to prevent CGRPR activation and its resulting effects. So far, FDA approved monoclonal antibodies and small molecule gepant inhibitors are considered for the treatment of acute or chronic migraine symptoms. However, most of these gepants have severe side effects. Thus, in this study, a virtual drug repurposing approach is applied to CGRPR to find alternative or better molecules that would have a potential to inhibit or block the CLR - RAMP1 interface compared to known gepant molecules. A small molecule library of FDA-approved molecules was screened in these two different binding sites, further simulations were performed and analyzed. The objectives of this study are (i) to repurpose an FDA-approved drug having more potent features for CGRPR inhibition compared to gepants, and (ii) to examine whether the transmembrane binding site (site 2) accepts small molecules or small peptide analogues for binding. As a result of this extensive in silico analysis, two molecules were identified, namely pentagastrin and leuprorelin. It is shown that FDA approved compound rimegepant and the identified pentagastrin molecules form and maintain the interactions through CLR W72 and RAMP1 W74, which are the residues revealed to have an important role in CGRPR antagonism at binding site 1. At binding site 2, the interactions needed to be formed for CGRP binding are not captured by rimegepant nor leuprorelin, yet leuprorelin forms more interactions throughout the simulations, meaning that small molecules are also capable of binding to site 2. Moreover, it is found that the crucial interactions for receptor signaling and heterodimerization occurred between CLR and RAMP1 interface are disrupted more with the ligands bound to ectodomain site, rather than the transmembrane domain. These findings of pentagastrin and leuprorelin molecules are recommended to be considered in further de novo drug development and/or experimental studies related to CGRPR signaling blockade and antagonism.