Targeted protein degradation has become a popular strategy to expand the druggable proteome, but therapeutic options for membrane proteins are limited. Sun et al. have now developed R-spondin chimeras (ROTACs) that effectively mediate lysosomal degradation of PD-L1, thus providing a modular platform that may be applicable to other membrane proteins.

Aberrant expression of transmembrane mucins promotes tumor progression and interferes with immunological and medicinal elimination of cancer cells. In a recent article, Pedram et al. directed an attenuated bacterial mucin-specific protease to HER2-positive tumor cells and observed decreased tumor growth rates and extended survival of mice bearing HER2-positive tumors.

Targeted protein degradation (TPD) technologies, particularly proteolysis-targeting chimeras (PROTACs), have emerged as a significant advancement in drug discovery. However, several hurdles - such as the difficulty of identifying suitable ligands for traditionally undruggable proteins, poor solubility and impermeability, nonspecific biodistribution, and on-target off-tissue toxicity - present challenges to their clinical applications. Aptamers are promising ligands for broad-ranging molecular recognition. Utilizing aptamers in TPD has shown potential advantages in overcoming these challenges. Here, we provide an overview of recent developments in aptamer-based TPD, emphasizing their potential to achieve targeted delivery and their promise for the spatiotemporal degradation of undruggable proteins. We also discuss the challenges and future directions of aptamer-based TPD with the goal of facilitating their clinical applications.

Timely intervention of preventative and therapeutic measures abated a 2022 mpox global outbreak. However, the high transmissibility and unique pathological characteristics of mpox demand further investigation. Here, we discuss the potentials of human skin-on-a-chip as a valuable model for mpox disease evaluation, to achieve in-depth physiological understanding and desirable therapeutic predictive capabilities.

Metastatic colorectal cancer (mCRC) remains a lethal disease with an approximately 14% 5-year survival rate. While early-stage colorectal cancer (CRC) can be cured by surgery with or without adjuvant chemotherapy, mCRC cannot be eradicated due to a large burden of disseminated cancer cells comprising therapy-resistant metastasis-competent cells. To address this gap, recent studies have focused on further elucidating the molecular mechanisms underlying colorectal metastasis and recognizing the limitations of available therapeutic interventions. In this review, we discuss newfound factors that regulate CRC cell dissemination and colonization of distant organs, such as genetic mutations, identification of metastasis-initiating cells (MICs), epithelial-mesenchymal transition (EMT), and the tumor microenvironment (TME). We also review current treatments for mCRC, therapeutic regimens undergoing clinical trials, and trending preclinical studies being investigated to target treatment-resistant mCRC.

Activation of the transcription factor nuclear factor erythroid 2-related factor 2 (NRF2) is emerging as an attractive therapeutic approach to counteract oxidative stress, inflammation, and metabolic imbalances. These processes underpin many chronic pathologies with unmet therapeutic needs, including neurodegenerative disorders and metabolic diseases. As the NRF2 field transitions into the clinical phase of its evolution, the need for an understanding of the factors influencing NRF2 pharmacology has never been greater. In this opinion article we describe the rationale for targeting NRF2, summarise the recent advances in drug development of NRF2 modulators, and reflect on the remaining challenges in realising the full clinical potential of NRF2 as a therapeutic target.

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

The orphan G-protein-coupled receptor 35 (GPR35), although poorly characterised, is attracting considerable interest as a therapeutic target. Marked differences in pharmacology between human and rodent orthologues of the receptor and a dearth of antagonists with affinity for mouse and rat GPR35 have previously restricted the use of preclinical disease models. The development of improved ligands, novel transgenic knock-in mouse lines, and detailed analysis of the disease relevance of single-nucleotide polymorphisms (SNPs) have greatly enhanced understanding of the key roles of GPR35 and have stimulated efforts towards disease-targeted proof-of-concept studies. In this opinion article, new information on the biology of the receptor is considered, whilst insight into how GPR35 is currently being assessed for therapeutic utility - in areas ranging from inflammatory bowel diseases to nonalcoholic steatohepatitis and various cancers - is also provided.

Mas-related G protein-coupled receptor (MRGPR) family members play important roles in the sensation of noxious stimuli and represent novel targets for the treatment of itch and pain. MRGPRs recognize a diversity of agonists and display complicated downstream signaling profiles, high sequence diversity across species, and many polymorphisms in humans. The recent structural advances on MRGPRs reveal unique structural features and diverse agonist recognition modes of this receptor family, which should facilitate the structure-based drug discovery at MRGPRs. In addition, the newly discovered ligands also provide valuable tools to explore the function and the therapeutic potential of MRGPRs. In this review, we discuss these progresses in our understanding of MRGPRs and highlight the challenges and potential opportunities for the future drug discovery at these receptors.

Pharmaceutical 3D printing (3DP) has attracted significant interest over the past decade for its ability to produce personalised medicines on demand. However, current quality control (QC) requirements for traditional large-scale pharmaceutical manufacturing are irreconcilable with the production offered by 3DP. The US Food and Drug Administration (FDA) and the UK Medicines and Healthcare Products Regulatory Agency (MHRA) have recently published documents supporting the implementation of 3DP for point-of-care (PoC) manufacturing along with regulatory hurdles. The importance of process analytical technology (PAT) and non-destructive analytical tools in translating pharmaceutical 3DP has experienced a surge in recognition. This review seeks to highlight the most recent research on non-destructive pharmaceutical 3DP analysis, while also proposing plausible QC systems that complement the pharmaceutical 3DP workflow. In closing, outstanding challenges in integrating these analytical tools into pharmaceutical 3DP workflows are discussed.

The tumor suppressor p53 is the most frequently mutated protein in human cancer and tops the list of high-value precision oncology targets. p53 prevents initiation and progression of cancer by inducing cell-cycle arrest and various forms of cell death. Tumors have thus evolved ways to inactivate p53, mainly by TP53 mutations or by hyperactive p53 degradation. This review focuses on two types of p53 targeting compounds, MDM2 antagonists and mutant p53 correctors. MDM2 inhibitors prevent p53 protein degradation, while correctors restore tumor suppressor activity of p53 mutants by enhancing thermodynamic stability. Herein we explore both novel and repurposed p53 targeting compounds, discuss their mode of action, and examine the challenges in advancing them to the clinic.

Autophagy is a cellular homeostasis mechanism that fuels the proliferation and survival of advanced cancers by degrading and recycling organelles and proteins. Preclinical studies have identified that within an established tumor, tumor cell autophagy and host cell autophagy conspire to support tumor growth. A growing body of evidence suggests that autophagy inhibition can augment the efficacy of chemotherapy, targeted therapy, or immunotherapy to enhance tumor shrinkage. First-generation autophagy inhibition trials in cancer using the lysosomal inhibitor hydroxychloroquine (HCQ) have produced mixed results but have guided the way for the development of more potent and specific autophagy inhibitors in clinical trials. In this review, we will discuss the role of autophagy in cancer, newly discovered molecular mechanisms of the autophagy pathway, the effects of autophagy modulation in cancer and host cells, and novel autophagy inhibitors that are entering clinical trials.

Iron accumulation has been associated with the etiology and progression of multiple neurodegenerative diseases (NDDs). The exact role of iron in these diseases is not fully understood, but an iron-dependent form of regulated cell death called ferroptosis could be key. Although there is substantial preclinical and clinical evidence that ferroptosis plays a role in NDD, there are still questions regarding how to target ferroptosis therapeutically, including which proteins to target, identification of clinically relevant biomarkers, and which patients might benefit most. Clinical trials of iron- and ferroptosis-targeted therapies are beginning to provide some answers, but there is growing interest in developing new ferroptosis inhibitors. We describe newly identified ferroptosis targets, opportunities, and challenges in NDD, as well as key considerations for progressing new therapeutics to the clinic.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

Artificial intelligence (AI)-based predictive models are being used to foster a precision medicine approach to treat complex chronic diseases such as autoimmune and autoinflammatory disorders (AIIDs). In the past few years the first models of systemic lupus erythematosus (SLE), primary Sjögren syndrome (pSS), and rheumatoid arthritis (RA) have been produced by molecular profiling of patients using omic technologies and integrating the data with AI. These advances have confirmed a complex pathophysiology involving multiple proinflammatory pathways and also provide evidence for shared molecular dysregulation across different AIIDs. I discuss how models are used to stratify patients, assess causality in pathophysiology, design drug candidates in silico, and predict drug efficacy in virtual patients. By relating individual patient characteristics to the predicted properties of millions of drug candidates, these models can improve the management of AIIDs through more personalized treatments.

Galectin (Gal)-3 is a β-galactoside-binding lectin emerging as a key player in cardiac, hepatic, renal, and pulmonary fibrosis and inflammation, respiratory infections caused by COVID-19, and neuroinflammatory disorders. Here, we review recent information highlighting Gal-3 as a relevant therapeutic target in these specific disease conditions. While a causal link was difficult to establish until now, we discuss how recent strategic breakthroughs allowed us to identify new-generation Gal-3 inhibitors with improved potency, selectivity, and bioavailability, and report their usefulness as valuable tools for proof-of-concept studies in various preclinical models of the aforementioned diseases, with emphasis on those actually in clinical stages. We also address critical views and suggestions intended to expand the therapeutic opportunities provided by this complex target.

Immunotherapies modulate the body's defense system to treat cancer. While these therapies have shown efficacy against multiple types of cancer, patient response rates are limited, and the off-target effects can be severe. Typical approaches in developing immunotherapies tend to focus on antigen targeting and molecular signaling, while overlooking biophysical and mechanobiological effects. Immune cells and tumor cells are both responsive to biophysical cues, which are prominent in the tumor microenvironment. Recent studies have shown that mechanosensing - including through Piezo1, adhesions, and Yes-associated protein (YAP) and transcriptional coactivator with PDZ-binding motif (TAZ) - influences tumor-immune interactions and immunotherapeutic efficacy. Furthermore, biophysical methods such as fluidic systems and mechanoactivation schemes can improve the controllability and manufacturing of engineered T cells, with potential for increasing therapeutic efficacy and specificity. This review focuses on leveraging advances in immune biophysics and mechanobiology toward improving chimeric antigen receptor (CAR) T-cell and anti-programmed cell death protein 1 (anti-PD-1) therapies.

The clinical efficacy and durability of KRASG12C-targeted therapies are limited by the development of resistance mechanisms. Here, we provide a review of recent KRASG12C-targeted therapy and immunotherapy-unifying strategies that utilize covalently modified peptide/MHC class I complexes as tumor-specific neoantigens to tag drug-resistant cancer cells for destruction with hapten-based immunotherapeutics.

Up to 1.5 million people die yearly from fungal disease, but the repertoire of antifungal drug classes is minimal and the incidence of drug resistance is rising rapidly. This dilemma was recently declared by the World Health Organization as a global health emergency, but the discovery of new antifungal drug classes remains excruciatingly slow. This process could be accelerated by focusing on novel targets, such as G protein-coupled receptor (GPCR)-like proteins, that have a high likelihood of being druggable and have well-defined biology and roles in disease. We discuss recent successes in understanding the biology of virulence and in structure determination of yeast GPCRs, and highlight new approaches that might pay significant dividends in the urgent search for novel antifungal drugs.

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.