Silvia Crescioli, Hélène Kaplon, Alicia Chenoweth
et al.
The Antibodies to Watch article series provides annual updates on commercial late-stage clinical development, regulatory review, and marketing approvals of antibody therapeutics. Since the first article was published in 2010, the late-stage pipeline has grown from 26 antibody therapeutics to over 200, while during the same time numerous molecules in late-stage studies either transitioned to regulatory review and were approved or were terminated. In this installment of the series, we recap first marketing approvals granted to 19 antibody therapeutics in 2025, discuss 26 molecules currently in regulatory review, including the bispecific antibody-drug conjugate izalontamab brengitecan, and predict which molecules of the 209 currently in the commercial late-stage pipeline might transition to regulatory review by the end of 2026. Most antibody therapeutics in the latter category are for non-cancer indications (16/21, 76%) and have a conventional format (13/21, 62%), but the category also includes numerous antibody-oligo or -drug conjugates, such as delpacibart etedesiran, delpacibart zotadirsen, zeleciment rostudirsen, sonesitatug vedotin, trastuzumab pamirtecan, and ifinatamab deruxtecan, as well as the bispecific petosemtamab. As antibody therapeutics development is a global enterprise, we also discuss trends in annual first approvals granted to antibody therapeutics in any country since 2010, stratified by the antibody’s country of origin, documenting the notable increases in the total number of first approvals and those approved first in China. Finally, to benchmark the time typically required for clinical development and regulatory review, we calculated this period for recently approved antibody therapeutic products stratified by their therapeutic area, mechanism of action, format, and country of origin. Our data show that the development and approval period were typically ~6 years, but on average this period was shorter for China-originated products.
Alfredo Torres-Benítez, José Erick Ortega-Valencia, Nicolás Jara-Pinuer
et al.
Lichens are an important source of diverse and unique secondary metabolites with recognized biological activities through experimental and computational procedures. The objective of this study is to investigate the metabolomic profile of the ethanolic extract of the Antarctic lichen <i>Gondwania regalis</i> and evaluate its antioxidant and antidiabetic activities with in vitro, in silico, and molecular dynamics simulations. Twenty-one compounds were tentatively identified for the first time using UHPLC/ESI/QToF/MS in negative mode. For antioxidant activity, the DPPH assay showed an IC<sub>50</sub> value of 2246.149 µg/mL; the total phenolic content was 31.9 mg GAE/g, the ORAC assay was 13.463 µmol Trolox/g, and the FRAP assay revealed 6.802 µmol Trolox/g. Regarding antidiabetic activity, enzyme inhibition yielded IC<sub>50</sub> values of 326.4513 µg/mL for pancreatic lipase, 19.49 µg/mL for α-glucosidase, and 585.216 µg/mL for α-amylase. Molecular docking identified sekikaic acid as the most promising compound, with strong binding affinities to catalytic sites, while molecular dynamics confirmed its stability and interactions. Toxicological and pharmacokinetic analyses supported its drug-like potential without significant risks. These findings suggest that the ethanolic extract of <i>Gondwania regalis</i> is a promising source of bioactive compounds for developing natural antioxidant and antidiabetic therapies.
Antonino Moschella, Soumaya Mourou, Samantha Perfler
et al.
ABSTRACT To plan future steps for the implementation and regulation of pharmacogenetic testing, any issue in the management of pharmacogenetic information by regulatory bodies must be identified. In this paper, an analysis of pharmacogenetic information in the summary of product characteristics (SmCPs) of drugs approved by Italian Drug Agency (AIFA) was conducted. Among 4214 SmCPs of 1063 active ingredients, 53.2% (n = 2240) included pharmacogenetic information in at least one section, most frequently for drugs in the Anatomical Therapeutic Chemical category “Antineoplastic and immunomodulatory agents”. To contextualize these data in the international scenario, a pharmacogenetic level of actionability, based on AIFA SmCPs, was assigned to 608 drug/gene pairs included in FDA's “Table of Pharmacogenomic Biomarkers in Drug Labels”, according to PharmGKB (The Pharmacogenomics Knowledge Base). Approximately 67% of drug/gene pairs were deemed classifiable: Based on SmCPs phrasing, for half of them the genetic testing was cataloged as “required” or “recommended” (mainly tumor somatic variants), whereas 40% as “actionable” (mostly PK/PD‐related germline variants). The comparison with other regulatory agencies highlighted a discordance in the assigned pharmacogenetic levels of actionability ranging from 1% to 14%. This discrepancy may also point out the need to rethink the language used in AIFA‐approved SmCPs to clarify whether a pharmacogenetic test is necessary or not and for which subjects it has been recommended. For the first time, a detailed evaluation and comparative analysis of the pharmacogenetic information on Italian SmCPs was presented, placing it in an international context and laying the groundwork for rethinking pharmacogenetic indications in AIFA‐approved SmCPs.
Therapeutics. Pharmacology, Public aspects of medicine
Motion disorders pose a significant global health concern and are often managed with pharmacological treatments that may lead to undesirable long-term effects. Current therapeutic strategies lack differentiation between healthy and unhealthy muscles in a patient, necessitating a targeted approach to distinguish between musculature. There is still no motion analyzer application for this purpose. Additionally, there is a deep gap in motion analysis software as some studies prioritize simulation, neglecting software needs, while others concentrate on computational aspects, disregarding simulation nuances. We introduce a comprehensive five-phase methodology to analyze the neuromuscular system of the lower body during gait. The first phase employs an innovative IoT-based method for motion signal capture. The second and third phases involve an agent-driven biomechanical model of the lower body skeleton and a model of human voluntary muscle. Thus, using an agent-driven approach, motion-captured signals can be converted to neural stimuli. The simulation results are then analyzed by our proposed ensemble neural network framework in the fourth step in order to detect abnormal motion in each joint. Finally, the results are shown by a userfriendly graphical interface which promotes the usability of the method. Utilizing the developed application, we simulate the neuromusculoskeletal system of some patients during the gait cycle, enabling the classification of healthy and pathological muscle activity through joint-based analysis. This study leverages cloud computing to create an infrastructure-independent application which is globally accessible. The proposed application enables experts to differentiate between healthy and unhealthy muscles in a patient by simulating his gait.
Hirofumi Tsuruta, Hiroyuki Yamazaki, Ryota Maeda
et al.
Antibodies are crucial proteins produced by the immune system to eliminate harmful foreign substances and have become pivotal therapeutic agents for treating human diseases. To accelerate the discovery of antibody therapeutics, there is growing interest in constructing language models using antibody sequences. However, the applicability of pre-trained language models for antibody discovery has not been thoroughly evaluated due to the scarcity of labeled datasets. To overcome these limitations, we introduce AVIDa-SARS-CoV-2, a dataset featuring the antigen-variable domain of heavy chain of heavy chain antibody (VHH) interactions obtained from two alpacas immunized with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike proteins. AVIDa-SARS-CoV-2 includes binary labels indicating the binding or non-binding of diverse VHH sequences to 12 SARS-CoV-2 mutants, such as the Delta and Omicron variants. Furthermore, we release VHHCorpus-2M, a pre-training dataset for antibody language models, containing over two million VHH sequences. We report benchmark results for predicting SARS-CoV-2-VHH binding using VHHBERT pre-trained on VHHCorpus-2M and existing general protein and antibody-specific pre-trained language models. These results confirm that AVIDa-SARS-CoV-2 provides valuable benchmarks for evaluating the representation capabilities of antibody language models for binding prediction, thereby facilitating the development of AI-driven antibody discovery. The datasets are available at https://datasets.cognanous.com.
Srivathsan Badrinarayanan, Chakradhar Guntuboina, Parisa Mollaei
et al.
Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.
Krishna Manoj, Theodore W. Grunberg, Domitilla Del Vecchio
The discovery of CRISPR-mediated gene activation (CRISPRa) has transformed the way in which we perform genetic screening, bioproduction and therapeutics through its ability to scale and multiplex. However, the emergence of loads on the key molecular resources constituting CRISPRa by the orthogonal short RNA that guide such resources to gene targets, couple theoretically independent CRISPRa modules. This coupling negates the ability of CRISPRa systems to concurrently regulate multiple genes independent of one another. In this paper, we propose to reduce this coupling by mitigating the loads on the molecular resources that constitute CRISPRa. In particular, we design a multi-variable controller that makes the concentration of these molecular resources robust to variations in the level of the short RNA loads. This work serves as a foundation to design and implement CRISPRa controllers for practical applications.
S. H. Mejias, A. L. Cortajarena, R. Mincigrucci
et al.
Understanding complex biological macromolecules, especially proteins, is vital for grasping their diverse chemical functions with direct impact in biology and pharmacology. While techniques like X-ray crystallography and cryo-electron microscopy have been valuable, they face limitations such as radiation damage and difficulties in crystallizing certain proteins. X-ray free-electron lasers (XFELs) offer promising solutions with their ultrafast, high-intensity pulses, potentially enabling structural determination before radiation damage occurs. However, challenges like low signal-to-noise ratio persist, particularly for single protein molecules. To address this, we propose a new method involving engineered protein scaffolds to create ordered arrays of proteins with controlled orientations, aiming at enhancing the signal at the detector. This innovative strategy has the potential to address signal limitations and protein crystallization issues, opening avenues for determining protein structures under physiological conditions. Moreover, it holds promise for studying conformational changes resulting from photo-induced changes, protein-drug and/or protein-protein interactions. Indeed, the prediction of protein-protein interactions, fundamental to numerous biochemical and cellular processes, and the time-dependent conformational changes they undergo, continue to pose a considerable challenge in biology and biochemistry.
We develop a set of algorithms to solve a broad class of Design of Experiment (DoE) problems efficiently. Specifically, we consider problems in which one must choose a subset of polymers to test in experiments such that the learning of the polymeric design rules is optimal. This subset must be selected from a larger set of polymers permissible under arbitrary experimental design constraints. We demonstrate the performance of our algorithms by solving several pragmatic nucleic acid therapeutics engineering scenarios, where limitations in synthesis of chemically diverse nucleic acids or feasibility of measurements in experimental setups appear as constraints. Our approach focuses on identifying optimal experimental designs from a given set of experiments, which is in contrast to traditional, generative DoE methods like BIBD. Finally, we discuss how these algorithms are broadly applicable to well-established optimal DoE criteria like D-optimality.
It is obvious that both epigenetic and non-epigenetic actors contribute to tumorigenesis in chondrosarcomas and more generally in other cancers. Thus, the main altered pathways in chondrosarcomas are now well established and include both epigenetic and non-epigenetic pathways such as the PI3K-AKT signaling, EGFR overexpression, SPARC overexpression, c-myc overexpression, IHH/GLI1 axis, loss of Rb function, HIF1-alpha stabilization, IDH1 mutations, hypermethylation and SIRT1. This review aims to provide a detailed analysis of these pathways and highlights recurrent interactions between non-epigenetic and epigenetic actors in chondrosarcomas, raising the intriguing possibility of developing therapeutics targeting both epigenetic and non-epigenetic actors and supporting data from previous studies. Finally, we propose some strategies for targeting chondrosarcomas in vivo based on properties of this tumor.
Lumbar spinal stenosis (LSS) is caused by the compression of the nerve root or cauda equina nerve by stenosis of the lumbar spinal canal or intervertebral foramen, and is manifested as chronic low back and leg pain. Danlu Tongdu (DLTD) tablets can relieve chronic pain caused by LSS, but the molecular mechanism remains largely unknown. In this study, the potential molecular mechanism of DLTD tablets in the treatment of LSS was firstly predicted by network pharmacology method. Results showed that DLTD functions in regulating anti-oxidative, apoptosis, and inflammation signaling pathways. Furthermore, the flow cytometry results showed that DLTD tablets efficiently reduced ROS content and inhibited rat neural stem cell apoptosis induced by hydrogen peroxide. DLTD also inhibited the mitochondrial membrane potential damage induced by hydrogen peroxide. Elisa analysis showed that DLTD induced cell cycle related protein, CDK2 and CDK4 and reduced CDKN1A protein expression level. Taken together, our study provided new insights of DLTD in treating LSS through reducing ROS content, decreasing apoptosis by inhibiting CDKN1A and promoting CDK2 and CDK4 expression levels.
Gabriel Monteiro da Silva, Jennifer Y. Cui, David C. Dalgarno
et al.
This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' single ground state conformations and is limited in its ability to predict fold switching and the effects of mutations on conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different conformations of proteins and even accurately predict changes in those populations induced by mutations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with accuracies in excess of 80%. Our method offers a fast and cost-effective way to predict protein conformations and their relative populations at even single point mutation resolution, making it a useful tool for pharmacology, analyzing NMR data, and studying the effects of evolution.
Cancer is fueled by the aberrant activity of oncogenic and tumor suppressive pathways. Transcriptional dysregulation of these pathways play a major role both in the genesis and development of cancer. Dysregulation of transcriptional programs can be mediated by genetic and epigenetic alterations targeting both protein coding genes and non-coding regulatory elements like enhancers and super-enhancers. Super-enhancers, characterized as large clusters of enhancers in close proximity, have been identified as essential oncogenic drivers required for the maintenance of cancer cell identity. As a result, cancer cells are often addicted to the super-enhancer driven transcriptional programs. Furthermore, pharmacological inhibitors targeting key components of super-enhancer assembly and activation have shown great promise in reducing tumor growth and proliferation in several pre-clinical tumor models. This article reviews the current understanding of super-enhancer assembly and activation, the different mechanisms by which cancer cells acquire oncogenic super-enhancers and, finally, the potential of targeting super-enhancers as future therapeutics.
Nshunge Musheshe, Asmaa Oun, Angélica María Sabogal-Guáqueta
et al.
Exchange proteins directly activated by cAMP (Epac) proteins are implicated in a wide range of cellular functions including oxidative stress and cell survival. Mitochondrial-dependent oxidative stress has been associated with progressive neuronal death underlying the pathology of many neurodegenerative diseases. The role of Epac modulation in neuronal cells in relation to cell survival and death, as well as its potential effect on mitochondrial function, is not well established. In immortalized hippocampal (HT-22) neuronal cells, we examined mitochondria function in the presence of various Epac pharmacological modulators in response to oxidative stress due to ferroptosis. Our study revealed that selective pharmacological modulation of Epac1 or Epac2 isoforms, exerted differential effects in erastin-induced ferroptosis conditions in HT-22 cells. Epac1 inhibition prevented cell death and loss of mitochondrial integrity induced by ferroptosis, while Epac2 inhibition had limited effects. Our data suggest Epac1 as a plausible therapeutic target for preventing ferroptosis cell death associated with neurodegenerative diseases.