Anticancer drugs: How to select small molecule combinations?

Tumors and their microenvironments: Learning from pediatric brain pathologies

Early clues to tumors and their microenvironments come from embryonic development. Here we review the literature and consider whether the embryonic brain and its pathologies can serve as a better model. Among embryonic organs, the brain is the most heterogenous and complex, with multiple lineages leading to wide spectrum of cell states and types. Its dysregulation promotes neurodevelopmental brain pathologies and pediatric tumors. Embryonic brain pathologies point to the crucial importance of spatial heterogeneity over time, akin to the tumor microenvironment. Tumors dedifferentiate through genetic mutations and epigenetic modulations; embryonic brains differentiate through epigenetic modulations. Our innovative review proposes learning developmental brain pathologies to target tumor evolution-and vice versa. We describe ways through which tumor pharmacology can learn from embryonic brains and their pathologies, and how learning tumor, and its microenvironment, can benefit targeting neurodevelopmental pathologies. Examples include pediatric low-grade versus high-grade brain tumors as in rhabdomyosarcomas and gliomas.

Pioneer in Molecular Biology: Conformational Ensembles in Molecular Recognition, Allostery, and Cell Function

In 1978, for my PhD, I developed the efficient O(n3) dynamic programming algorithm for the-then open problem of RNA secondary structure prediction. This algorithm, now dubbed the "Nussinov algorithm", "Nussinov plots", and "Nussinov diagrams", is still taught across Europe and the U.S. As sequences started coming out in the 1980s, I started seeking genome-encoded functional signals, later becoming a bioinformatics trend. In the early 1990s I transited to proteins, co-developing a powerful computer vision-based docking algorithm. In the late 1990s, I proposed the foundational role of conformational ensembles in molecular recognition and allostery. At the time, conformational ensembles and free energy landscapes were viewed as physical properties of proteins but were not associated with function. The classical view of molecular recognition and binding was based on only two conformations captured by crystallography: open and closed. I proposed that all conformational states preexist. Proteins always have not one folded form-nor two-but many folded forms. Thus, rather than inducing fit, binding can work by shifting the ensembles between states, and this shifting, or redistributing the ensembles to maintain equilibrium, is the origin of the allosteric effect and protein, thus cell, function. This transformative paradigm impacted community views in allosteric drug design, catalysis, and regulation. Dynamic conformational ensemble shifts are now acknowledged as the origin of recognition, allostery, and signaling, underscoring that conformational ensembles-not proteins-are the workhorses of the cell, pioneering the fundamental idea that dynamic ensembles are the driving force behind cellular processes. Nussinov was recognized as pioneer in molecular biology by JMB.

Exploring the antitumorigenic properties of agro-food byproducts: A comprehensive scientific review

Natural byproducts have garnered significant attention for their potential antitumorigenic properties. The current scenario sketched by the goals of circular economy approaches the research towards the utilization of agro-waste biomasses as valuable source of biological active metabolites. This comprehensive scientific review explores the various mechanisms through which these natural compounds exert anticancer effects, including apoptosis induction, cell cycle arrest, inhibition of angiogenesis, and suppression of metastasis. The review highlights key bioactive molecules such as polyphenols, flavonoids, alkaloids, and terpenoids, examining their molecular interactions with cancer cells. Furthermore, the potential of these natural byproducts as adjuvant therapies in combination with conventional treatments is discussed. By summarizing recent advancements and identifying future research directions, this review underscores the promise of natural byproducts from as a source of novel anticancer agents. A specific section is dedicated to outline the role of innovative materials, such as nanoparticles, hydrogels, and biopolymers, that are being developed to enhance the delivery and efficacy of active components. These carriers offer improved stability, targeted delivery, and controlled release of natural compounds, maximizing their therapeutic potential while minimizing side effects.

Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge

Charting future innovations is challenging. Yet, allosteric and orthosteric anticancer drugs are undergoing a revolution and taxing unresolved dilemmas await. Among the imaginative innovations, here we discuss cereblon and thalidomide derivatives as a means of recruiting neosubstrates and their degradation, allosteric heterogeneous bifunctional drugs like PROTACs, drugging phosphatases, inducers of targeted posttranslational protein modifications, antibody-drug conjugates, exploiting membrane interactions to increase local concentration, stabilizing the folded state, and more. These couple with harnessing allosteric cryptic pockets whose discovery offers more options to modulate the affinity of orthosteric, active site inhibitors. Added to these are strategies to counter drug resistance through drug combinations co-targeting pathways to bypass signaling blockades. Here, we discuss on the molecular and cellular levels, such inspiring advances, provide examples of their applications, their mechanisms and rational. We start with an overview on difficult to target proteins and their properties-rarely, if ever-conceptualized before, discuss emerging innovative drugs, and proceed to the increasingly popular allosteric cryptic pockets-their advantages-and critically, issues to be aware of. We follow with drug resistance and in-depth discussion of tumor heterogeneity. Heterogeneity is a hallmark of highly aggressive cancers, the core of drug resistance unresolved challenge. We discuss potential ways to target heterogeneity by predicting it. The increase in experimental and clinical data, computed (cell-type specific) interactomes, capturing transient cryptic pockets, learned drug resistance, workings of regulatory mechanisms, heterogeneity, and resistance-based cell signaling drug combinations, assisted by AI-driven reasoning and recognition, couple with creative allosteric drug discovery, charting future innovations.

Molecular principles underlying aggressive cancers

Aggressive tumors pose ultra-challenges to drug resistance. Anti-cancer treatments are often unsuccessful, and single-cell technologies to rein drug resistance mechanisms are still fruitless. The National Cancer Institute defines aggressive cancers at the tissue level, describing them as those that spread rapidly, despite severe treatment. At the molecular, foundational level, the quantitative biophysics discipline defines aggressive cancers as harboring a large number of (overexpressed, or mutated) crucial signaling proteins in major proliferation pathways populating their active conformations, primed for their signal transduction roles. This comprehensive review explores highly aggressive cancers on the foundational and cell signaling levels, focusing on the differences between highly aggressive cancers and the more treatable ones. It showcases aggressive tumors as harboring massive, cancer-promoting, catalysis-primed oncogenic proteins, especially through certain overexpression scenarios, as predisposed aggressive tumor candidates. Our examples narrate strong activation of ERK1/2, and other oncogenic proteins, through malfunctioning chromatin and crosslinked signaling, and how they activate multiple proliferation pathways. They show the increased cancer heterogeneity, plasticity, and drug resistance. Our review formulates the principles underlying cancer aggressiveness on the molecular level, discusses scenarios, and describes drug regimen (single drugs and drug combinations) for PDAC, NSCLC, CRC, HCC, breast and prostate cancers, glioblastoma, neuroblastoma, and leukemia as examples. All show overexpression scenarios of master transcription factors, transcription factors with gene fusions, copy number alterations, dysregulation of the epigenetic codes and epithelial-to-mesenchymal transitions in aggressive tumors, as well as high mutation loads of vital upstream signaling regulators, such as EGFR, c-MET, and K-Ras, befitting these principles.

Reevaluating Calculus bovis: Modulating the liver cancer immune microenvironment via the Wnt/β-catenin pathway

In this article, we comment on the work published by Huang et al, which explores the mechanisms by which Calculus bovis (CB) modulates the liver cancer immune microenvironment via the Wnt/β-catenin signalling pathway. The study demonstrates that active components in CB effectively inhibit the activation of the Wnt/β-catenin pathway, significantly reducing the polarization of M2 tumor-associated macrophages. Both in vivo and in vitro experiments have validated the anti-tumour effects of CB, revealing its complex mechanisms of action through the modulation of immune cell functions within the tumour microenvironment. This article highlights CB's therapeutic potential in liver cancer treatment and calls for further investigations into its mechanisms and clinical applications to develop safer, more effective options for patients. The study also revealed that key components of CB, such as bilirubin and bile acids, inhibit tumour cell proliferation and promote apoptosis through multiple pathways. Future research should explore the mechanisms of action of CB and its potential integration with existing treatments to improve the therapeutic outcomes of liver cancer patients. With multidisciplinary collaboration and advanced research, CB could become a key component of comprehensive liver cancer treatment, offering new hope for patients.

System level network data and models attack cancer drug resistance

Drug resistance is responsible for >90% of cancer related deaths. Cancer drug resistance is a system level network phenomenon covering the entire cell. Small-scale interactomes and signalling network models of drug resistance guide directed drug development. Recently, proteome-wide human interactome and signalling network data have become available, which have been extended by drug-target interactions, drug resistance-inducing mutations, as well as by several cancer and drug resistance-related multi-omics datasets. System level signalling network models have become available examining therapy resistance, performing in silico clinical trials, and conducting large, in silico drug combination screens. Drug resistance network data and models have become interoperable and reliable. These advances paved the road for building proteome-wide drug resistance models.

Cancer drug resistance as learning of signaling networks

Drug resistance is a major cause of tumor mortality. Signaling networks became useful tools for driving pharmacological interventions against cancer drug resistance. Signaling datasets now cover the entire human cell. Recently, network adaptation became understood as a learning process. We review rapidly increasing evidence showing that the development of cancer drug resistance can be described as learning of signaling networks. During drug adaptation, the network forgets drug-affected pathways by desensitization and relearns by strengthening alternative pathways. Thus, resistant cancer cells develop a drug resistance memory. We show that all key players of cellular learning (i.e., IDPs, protein translocation, microRNAs/lncRNAs, scaffolding proteins and epigenetic/chromatin memory) have important roles in the development of cancer drug resistance. Moreover, all of them are central components of the epithelial-mesenchymal transition leading to metastases and resistance. Phenotypic plasticity was recently listed as a hallmark of cancer. We review how network plasticity induces rare, pre-existent drug-resistant cells in the absence of drug treatment. Key network methods assessing the development of drug resistance and network pharmacological interventions against drug resistance are summarized. Finally, we highlight the class of cellular memory drugs affecting cellular learning and forgetting, and we summarize current challenges to prevent or break drug resistance using network models.

Furopyridine Derivatives as Potent Inhibitors of the Wild Type, L858R/T790M, and L858R/T790M/C797S EGFR

The treatment of patients with nonsmall cell lung cancer (NSCLC) using epidermal growth factor receptor (EGFR) inhibitors is complicated by drug-sensitive activating L858R/T790M and L858R/T790M/C797S mutations. To overcome drug resistance, a series of furopyridine (PD) compounds were virtually screened to identify potent EGFR inhibitors using molecular docking and molecular dynamics (MD) simulations based on the solvated interaction energy (SIE) method. Several PD compounds identified from virtual screening demonstrated the potential to suppress both wild-type and mutant forms of EGFR, with IC50 values in the nanomolar range. Among these, PD18 and PD56 exhibited highly potent inhibitory activity against both wild-type and mutant forms of EGFR, surpassing the efficacy of known drugs. Additionally, both PD compounds were cytotoxic to NSCLC cell lines (A549 and H1975) while being nontoxic to normal cell lines (Vero). The interaction mechanisms of both PD compounds complexed with wild-type and mutant forms of EGFR were elucidated through 500 ns molecular dynamics simulations. The predicted binding affinity from molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) correlated well with the experimental binding affinity derived from IC50 values. Furthermore, it was observed that van der Waals interactions, rather than electrostatic interactions, played a significant role in interacting with EGFR's active site. The strong inhibitory activity against EGFR was attributed to two key residues, M793 and S797, via hydrogen bonding, corresponding with lower solvent accessibility and a higher number of atomic contacts. Therefore, these potent compounds could be developed as promising drugs targeting both wild-type and mutant EGFR for the treatment of NSCLC.

Global hinge sites of proteins as target sites for drug binding

Hinge sites of proteins play a key role in mediating conformational mechanics. Among them, those involved in the most collective modes of motion, also called global hinges, are of particular interest, as they support cooperative rearrangements that are often functional. Yet, the utility of targeting global hinges for modulating function remains to be established. We present here a systematic study of a series of proteins resolved in drug-bound forms to examine the probabilistic occurrence of spatial overlaps between hinge sites and drug-binding pockets. Our analysis reveals a high propensity of drug binding to hinge sites compared to random. Notably, one-third of currently approved drugs are colocalized with hinge sites. These mechanosensitive sites are predictable by simple models such as the Gaussian Network Model. Their targeting thus emerges as a viable strategy for developing a new class of drugs that would exploit and modulate the target proteins' intrinsic dynamics, and potentially alleviate drug-resistance when used in combination with orthosteric or allosteric drugs.

The value of protein allostery in rational anticancer drug design: an update

INTRODUCTION

Allosteric drugs are advantageous. However, they still face hurdles, including identification of allosteric sites that will effectively alter the active site. Current strategies largely focus on identifying pockets away from the active sites into which the allosteric ligand will dock and do not account for exactly how the active site is altered. Favorable allosteric inhibitors dock into sites that are nearby the active sites and follow nature, mimicking diverse allosteric regulation strategies.

AREAS COVERED

The following article underscores the immense significance of allostery in drug design, describes current allosteric strategies, and especially offers a direction going forward. The article concludes with the authors' expert perspectives on the subject.

EXPERT OPINION

To select a productive venue in allosteric inhibitor development, we should learn from nature. Currently, useful strategies follow this route. Consider, for example, the mechanisms exploited in relieving autoinhibition and in harnessing allosteric degraders. Mimicking compensatory, or rescue mutations may also fall into such a thesis, as can molecular glues that capture features of scaffolding proteins. Capturing nature and creatively tailoring its mimicry can continue to innovate allosteric drug discovery.

Review: Cancer and neurodevelopmental disorders: multi-scale reasoning and computational guide

The connection and causality between cancer and neurodevelopmental disorders have been puzzling. How can the same cellular pathways, proteins, and mutations lead to pathologies with vastly different clinical presentations? And why do individuals with neurodevelopmental disorders, such as autism and schizophrenia, face higher chances of cancer emerging throughout their lifetime? Our broad review emphasizes the multi-scale aspect of this type of reasoning. As these examples demonstrate, rather than focusing on a specific organ system or disease, we aim at the new understanding that can be gained. Within this framework, our review calls attention to computational strategies which can be powerful in discovering connections, causalities, predicting clinical outcomes, and are vital for drug discovery. Thus, rather than centering on the clinical features, we draw on the rapidly increasing data on the molecular level, including mutations, isoforms, three-dimensional structures, and expression levels of the respective disease-associated genes. Their integrated analysis, together with chromatin states, can delineate how, despite being connected, neurodevelopmental disorders and cancer differ, and how the same mutations can lead to different clinical symptoms. Here, we seek to uncover the emerging connection between cancer, including pediatric tumors, and neurodevelopmental disorders, and the tantalizing questions that this connection raises.