bims-cepepe 2024-05-05 papers

Issue of 2024‒05‒05
five papers selected by
Henry Lamb, Queensland University of Technology

J Biol Chem. 2024 Apr 26. pii: S0021-9258(24)01831-3. [Epub ahead of print] 107330

Discovery and development of macrocyclic peptide modulators of the cannabinoid 2 receptor.

Nataša Tomašević, Fabiola Susanna Emser, Edin Muratspahić, Jasmin Gattringer, Simon Hasinger, Roland Hellinger, Peter Keov, Manuel Felkl, Jürg Gertsch, Christian F W Becker, Christian W Gruber.

The cannabinoid-type 2 receptor (CB2R), a G protein-coupled receptor (GPCR), is an important regulator of immune cell function and a promising target to treat chronic inflammation and fibrosis. While CB2R is typically targeted by small molecules, including endo-, phyto- and synthetic cannabinoids, peptides - owing to their size - may offer a different interaction space to facilitate differential interactions with the receptor. Here we explore plant-derived cyclic cystine-knot peptides as ligands of the CB2R. Cyclotides are known for their exceptional biochemical stability. Recently they gained attention as GPCR modulators and as templates for designing peptide ligands with improved pharmacokinetic properties over linear peptides. Cyclotide-based ligands for CB2R were profiled based on a peptide-enriched extract library comprising nine plants. Employing pharmacology-guided fractionation and peptidomics we identified cyclotide vodo-C1 from sweet violet (Viola odorata) as a full agonist of CB2R with an affinity (Ki) of 1μM and a potency (EC50) of 8μM. Leveraging deep learning networks we verified the structural topology of vodo-C1 and modelled its molecular volume in comparison to the CB2R ligand binding pocket. In a fragment-based approach we designed and characterized vodo-C1-based bicyclic peptides (vBCL1-4), aiming to reduce size and improve potency. Opposite to vodo-C1, the vBCL peptides lacked the ability to activate the receptor but acted as negative allosteric modulators or neutral antagonists of CB2R. This study introduces a macrocyclic peptide phytocannabinoid, which served as template for the development of synthetic CB2R peptide modulators. These findings offer opportunities for future peptide-based probe and drug development at cannabinoid receptors.

Keywords: G protein-coupled receptor; allosteric modulator; cannabinoid type 2 receptor; peptide; plant

DOI: https://doi.org/10.1016/j.jbc.2024.107330

ACS Omega. 2024 Apr 23. 9(16): 18608-18616

Two Novel [68Ga]Ga-Labeled Radiotracers Based on Metabolically Stable [Sar11]RM26 Antagonistic Peptide for Diagnostic Positron Emission Tomography Imaging of GRPR-Positive Prostate Cancer.

Panagiotis Kanellopoulos, Ekaterina Bezverkhniaia, Ayman Abouzayed, Ulrika Rosenström, Vladimir Tolmachev, Anna Orlova.

Gastrin releasing peptide receptor (GRPR) is overexpressed in prostate cancer (PC-3) and can be used for diagnostic purposes. We herein present the design and preclinical evaluation of two novel NOTA/NODAGA-containing peptides suitable for labeling with the positron emission tomography (PET) radionuclide Ga-68. These analogs are based on the previously reported GRPR-antagonist DOTAGA-PEG2-[Sar11]RM26, developed for targeted radiotheraostic applications. Both NOTA-PEG2-[Sar11]RM26 and NODAGA-PEG2-[Sar11]RM26 were successfully labeled with Ga-68 and evaluated in vitro and in vivo using PC-3 cell models. Both, [68Ga]Ga-NOTA-PEG2-[Sar11]RM26 and [68Ga]Ga-NODAGA-PEG2-[Sar11]RM26 displayed high metal-chelate stability in phosphate buffered saline and against the EDTA-challenge. The two [68Ga]Ga-labeled conjugates demonstrated highly GRPR-mediated uptake in vitro and in vivo and exhibited a slow internalization over time, typical for radioantagonistis. The [natGa]Ga-loaded peptides displayed affinity in the low nanomole range for GRPR in competition binding experiments. The new radiotracers demonstrated biodistribution profiles suitable for diagnostic imaging shortly after administration with fast background clearance. Their high tumor uptake (13 ± 1 and 15 ± 3% IA/g for NOTA and NODAGA conjugates, respectively) and high tumor-to-blood ratios (60 ± 10 and 220 ± 70, respectively) 3 h pi renders them promising PET tracers for use in patients. Tumor-to-normal organ ratios were higher for [68Ga]Ga-NODAGA-PEG2-[Sar11]RM26 than for the NOTA-containing counterpart. The performance of the two radiopeptides was further supported with the PET/CT images. In conclusion, [68Ga]Ga-NODAGA-PEG2-[Sar11]RM26 is a promising PET imaging tracer for visualization of GRPR-expressing lesions with high imaging contrast shortly after administration.

DOI: https://doi.org/10.1021/acsomega.4c01348

J Chem Inf Model. 2024 May 03.

Breaking the Barriers: Machine-Learning-Based c-RASAR Approach for Accurate Blood-Brain Barrier Permeability Prediction.

Vinay Kumar, Arkaprava Banerjee, Kunal Roy.

The intricate nature of the blood-brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure-activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model's predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model's reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

DOI: https://doi.org/10.1021/acs.jcim.4c00433

Biochem Pharmacol. 2024 May 01. pii: S0006-2952(24)00235-1. [Epub ahead of print] 116252

ALDH1A1 confers resistance to RAF/MEK inhibitors in melanoma cells by maintaining stemness phenotype and activating PI3K/AKT signaling.

Valerio Ciccone, Vittoria Simonis, Cinzia Del Gaudio, Claudio Cucini, Marina Ziche, Lucia Morbidelli, Sandra Donnini.

The mitogen-activated protein kinase (MAPK/ERK) pathway is pivotal in controlling the proliferation and survival of melanoma cells. Several mutations, including those in BRAF, exhibit an oncogenic effect leading to increased cellular proliferation. As a result, the combination therapy of a MEK inhibitor with a BRAF inhibitor demonstrated higher efficacy and lower toxicity than BRAF inhibitor alone. This combination has become the preferred standard of care for tumors driven by BRAF mutations. Aldehyde dehydrogenase 1A1 (ALDH1A1) is a known marker of stemness involved in drug resistance in several type of tumors, including melanoma. This study demonstrates that melanoma cells overexpressing ALDH1A1 displayed resistance to vemurafenib and trametinib through the activation of PI3K/AKT signaling instead of MAPK axis. Inhibition of PI3K/AKT signaling partially rescued sensitivity to the drugs. Consistently, pharmacological inhibition of ALDH1A1 activity downregulated the activation of AKT and partially recovered responsiveness to vemurafenib and trametinib. We propose ALDH1A1 as a new potential target for treating melanoma resistant to MAPK/ERK inhibitors.

Keywords: ALDH1A1; Melanoma; RAF/MEK inhibitors resistance; Stemness; Target therapy resistance

DOI: https://doi.org/10.1016/j.bcp.2024.116252

Methods Mol Biol. 2024 ;2806 153-185

Patient-Derived Xenograft Models for Translational Prostate Cancer Research and Drug Development.

Lisa Kate Philp.

Patient-derived xenografts (PDXs) are a valuable preclinical research platform generated through transplantation of a patient's resected tumor into an immunodeficient or humanized mouse. PDXs serve as a high-fidelity avatar for both precision medicine and therapeutic testing against the cancer patient's disease state. While PDXs show mixed response to initial establishment, those that successfully engraft and can be sustained with serial passaging form a useful tool for basic and translational prostate cancer (PCa) research. While genetically engineered mouse (GEM) models and human cancer cell lines, and their xenografts, each play beneficial roles in discovery science and initial drug screening, PDX tumors are emerging as the gold standard approach for therapeutic proof-of-concept prior to entering clinical trial. PDXs are a powerful platform, with PCa PDXs shown to represent the original patient tumor cell population and architecture, histopathology, genomic and transcriptomic landscape, and heterogeneity. Furthermore, PDX response to anticancer drugs in mice has been closely correlated to the original patient's susceptibility to these treatments in the clinic. Several PDXs have been established and have undergone critical in-depth characterization at the cellular and molecular level across multiple PCa tumor subtypes representing both primary and metastatic patient tumors and their inherent levels of androgen responsiveness and/or treatment resistance, including androgen-sensitive, castration resistant, and neuroendocrine PCa. Multiple PDX networks and repositories have been generated for the collaborative and shared use of these vital translational cancer tools. Here we describe the creation of a PDX maintenance colony from an established well-characterized PDX, best practice for PDX maintenance in mice, and their subsequent application in preclinical drug testing. This chapter aims to serve as a go to resource for the preparation and adoption of PCa PDX models in the research laboratory and for their use as a valuable preclinical platform for translational research and therapeutic agent development.

Keywords: Cancer; Drug development; Patient specimens; Patient-derived xenografts (PDXs); Preclinical models; Therapeutic agents; Translational research; Tumor models

DOI: https://doi.org/10.1007/978-1-0716-3858-3_12