The rise in chronic diseases over the last century presents a significant health and economic burden globally. Here we apply evolutionary medicine and life history theory to better understand their development. We highlight an imbalanced metabolic axis of growth and proliferation (anabolic) versus maintenance and dormancy (catabolic), focusing on major mechanisms including IGF-1, mTOR, AMPK, and Klotho. We also relate this axis to the hyperfunction theory of aging, which similarly implicates anabolic mechanisms like mTOR in aging and disease. Next, we highlight the Brain-Body Energy Conservation model, which connects the hyperfunction theory with energetic trade-offs that induce hypofunction and catabolic health risks like impaired immunity. Finally, we discuss how modern environmental mismatches exacerbate this process. Following our review, we discuss future research directions to better understand health risk. This includes studying IGF-1, mTOR, AMPK, and Klotho and how they relate to health and aging in human subsistence populations, including with lifestyle shifts. It also includes understanding their role in the developmental origins of health and disease as well as the social determinants of health disparities. Further, we discuss the need for future studies on exceptionally long-lived species to understand potentially underappreciated trade-offs and costs that come with their longevity. We close with considering possible implications for therapeutics, including (1) compensatory pathways counteracting treatments, (2) a Goldilocks zone, in which suppressing anabolic metabolism too far introduces catabolic health risks, and (3) species constraints, in which therapeutics tested in shorter lived species with greater anabolic imbalance will be less effective in humans.
Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enriched in many types of cancer, particularly in hepatocellular carcinoma (HCC). The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions. Targeting CD19$^+$ macrophages with anti-CD19 chimeric antigen receptor T (CAR-T) cells inhibited HCC tumor growth. We identify PAX5 as a primary driver of up-regulated mitochondrial biogenesis in CD19$^+$ macrophages, which depletes cytoplasmic Ca$^{2+}$, leading to lysosomal deficiency and consequent accumulation of CD73 and PD-L1. Inhibiting CD73 or mitochondrial oxidation enhanced the efficacy of immune checkpoint blockade therapy in treating HCC, suggesting great promise for CD19$^+$ macrophage-targeting therapeutics.
Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad
et al.
Designing mRNA sequences is a major challenge in developing next-generation therapeutics, since it involves exploring a vast space of possible nucleotide combinations while optimizing sequence properties like stability, translation efficiency, and protein expression. While Generative Flow Networks are promising for this task, their training is hindered by sparse, long-horizon rewards and multi-objective trade-offs. We propose Curriculum-Augmented GFlowNets (CAGFN), which integrate curriculum learning with multi-objective GFlowNets to generate de novo mRNA sequences. CAGFN integrates a length-based curriculum that progressively adapts the maximum sequence length guiding exploration from easier to harder subproblems. We also provide a new mRNA design environment for GFlowNets which, given a target protein sequence and a combination of biological objectives, allows for the training of models that generate plausible mRNA candidates. This provides a biologically motivated setting for applying and advancing GFlowNets in therapeutic sequence design. On different mRNA design tasks, CAGFN improves Pareto performance and biological plausibility, while maintaining diversity. Moreover, CAGFN reaches higher-quality solutions faster than a GFlowNet trained with random sequence sampling (no curriculum), and enables generalization to out-of-distribution sequences.
Sierra Haile, Benjamin C. Balzer, Emily Egan
et al.
This article focuses on current and emerging therapeutics for CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy). CADASIL is an inherited vascular disease that impairs blood flow in the small cerebral vessels of the brain, leading to strokes and other neurological deficits. The disease is caused by a mutation in the NOTCH3 gene located on chromosome 19. NOTCH3 encodes a transmembrane receptor expressed on vascular smooth muscle cells. In CADASIL, mutations in the NOTCH3 gene lead to the accumulation and deposition of the receptor, affecting the number of cysteine residues in its extracellular domain. These mutations result in the loss or gain of a cysteine residue within the epidermal growth factor-like repeat (EGFr) domains of the NOTCH protein. Beyond traditional symptomatic treatments for stroke, this work highlights advances in disease modifying approaches including gene editing, cell therapies, and immune-based interventions aimed at altering the course of CADASIL. It also examines ongoing clinical trials and recent patents related to these novel strategies. In addition to summarizing diagnostic methods and molecular mechanisms, the article emphasizes the translational potential of current research and the experimental models driving therapeutic development. The goal is to offer a comprehensive overview of CADASIL and emerging interventions that hold promise for improving long-term outcomes.
Majid Jaberi-Douraki, Hossein Sholehrasa, Xuan Xu
et al.
The extraction and standardization of pharmacokinetic (PK) information from scientific literature remain significant challenges in computational pharmacology, which limits the reliability of data-driven models in drug development. Large language models (LLMs) have achieved remarkable progress in text understanding and reasoning, yet their adaptation to structured biomedical data, such as PK tables, remains constrained by heterogeneity, noise, and domain shift. To address these limitations, we propose HySim-LLM, a unified mathematical and computational framework that integrates embedding-weighted fine-tuning and manifold-aware denoising to enhance the robustness and interpretability of LLMs. We establish two theoretical results: (1) a similarity-weighted generalization bound that quantifies adaptation performance under embedding divergence, and (2) a manifold-based denoising guarantee that bounds loss contributions from noisy or off-manifold samples. These theorems provide a principled foundation for fine-tuning LLMs in structured biomedical settings. The framework offers a mathematically grounded pathway toward reliable and interpretable LLM adaptation for biomedical and data-intensive scientific domains.
Cornelia-Ioana Ilie, Angela Spoiala, Cristina Chircov
et al.
The gut microbiota dysbiosis that often occurs in cancer therapy requires more efficient treatment options to be developed. In this concern, the present research approach is to develop drug delivery systems based on magnetite nanoparticles (MNPs) as nanocarriers for bioactive compounds. First, MNPs were synthesized through the spraying-assisted coprecipitation method, followed by loading bee pollen or bee bread extracts and an antitumoral drug (5-fluorouracil/5-FU). The loaded-MNPs were morphologically and structurally characterized through transmission electron microscopy (TEM), selected area electron diffraction (SAED), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), Dynamic Light Scattering (DLS), and thermogravimetric analysis. UV-Vis spectroscopy was applied to establish the release profiles and antioxidant activity. Furthermore, the antibacterial and antitumoral activity of loaded-MNPs was assessed. The results demonstrate that MNPs with antioxidant, antibacterial, antiproliferative, and prebiotic properties are obtained. Moreover, the data highlight the improvement of 5-FU antibacterial activity by loading on the MNPs’ surface and the synergistic effects between the anticancer drug and phenolic compounds (PCs). In addition, the prolonged release behavior of PCs for many hours (70–75 h) after the release of 5-FU from the developed nanocarriers is an advantage, at least from the point of view of the antioxidant activity of PCs. Considering the enhancement of <i>L. rhamnosus</i> MF9 growth and antitumoral activity, this study developed promising drug delivery alternatives for colorectal cancer therapy.
Aurélien Dinh, Alexandre Bleibtreu, Clara Duran
et al.
Background: Meropenem–vaborbactam (MEM-VAB) is a novel carbapenem-beta-lactamase-inhibitor combination that demonstrates activity against carbapenem-resistant (CR) Gram-negative bacteria, and more specifically KPC-producers, since vaborbactam is an effective inhibitor of KPC enzymes in vitro. This study aimed to describe the initial uses and efficacy of MEM-VAB for compassionate treatment during the first 21 months following its early access in France. Method: A national multicenter retrospective study was conducted, including all patients who received at least one dose of MEM-VAB between 20 July 2020, and 5 April 2022. Clinical characteristics and outcomes were collected using a standardized questionnaire. The minimum inhibitory concentration (MIC) of antimicrobials, and complete genome sequencing of bacteria were performed when bacterial isolates were available. Results: Ultimately, 21 patients from 15 French hospitals were included in the study. The main indication for MEM-VAB treatment was respiratory tract infections (<i>n</i> = 9). The targeted bacteria included <i>Pseudomonas aeruginosa</i> (<i>n</i> = 12), <i>Klebsiella pneumoniae</i> (<i>n</i> = 3), <i>Enterobacter spp</i> (<i>n</i> = 3), <i>Citrobacter freundii</i> (<i>n</i> = 1), <i>Escherichia coli</i> (<i>n</i> = 1), and <i>Burkholderia multivorans</i> (<i>n</i> = 1). Overall, no significant advantage of vaborbactam over meropenem alone was observed across all strains of <i>P. aeruginosa</i> in terms of in vitro susceptibility. However, MEM-VAB demonstrated a notable impact, compared to carbapenem alone, on the MIC for the two KPC-3-producing <i>K. pneumoniae</i> and <i>B. multivorans</i>. Conclusions: MEM-VAB seems effective as a salvage treatment in compassionate use, but vaborbactam was shown to lack benefits compared to meropenem in treating <i>P. aeruginosa</i>-related infections. Therefore, it is crucial to compare meropenem to MEM-VAB MICs, particularly for <i>P. aeruginosa</i>, before prescribing MEM-VAB.
Drug-side effect prediction has become an essential area of research in the field of pharmacology. As the use of medications continues to rise, so does the importance of understanding and mitigating the potential risks associated with them. At present, researchers have turned to data-driven methods to predict drug-side effects. Drug-side effect prediction is a link prediction problem, and the related data can be described from various perspectives. To process these kinds of data, a multi-view method, called Multiple Kronecker RLS fusion-based link propagation (MKronRLSF-LP), is proposed. MKronRLSF-LP extends the Kron-RLS by finding the consensus partitions and multiple graph Laplacian constraints in the multi-view setting. Both of these multi-view settings contribute to a higher quality result. Extensive experiments have been conducted on drug-side effect datasets, and our empirical results provide evidence that our approach is effective and robust.
This paper presents a study on the integration of domain-specific knowledge in prompt engineering to enhance the performance of large language models (LLMs) in scientific domains. A benchmark dataset is curated to encapsulate the intricate physical-chemical properties of small molecules, their drugability for pharmacology, alongside the functional attributes of enzymes and crystal materials, underscoring the relevance and applicability across biological and chemical domains.The proposed domain-knowledge embedded prompt engineering method outperforms traditional prompt engineering strategies on various metrics, including capability, accuracy, F1 score, and hallucination drop. The effectiveness of the method is demonstrated through case studies on complex materials including the MacMillan catalyst, paclitaxel, and lithium cobalt oxide. The results suggest that domain-knowledge prompts can guide LLMs to generate more accurate and relevant responses, highlighting the potential of LLMs as powerful tools for scientific discovery and innovation when equipped with domain-specific prompts. The study also discusses limitations and future directions for domain-specific prompt engineering development.
Henry Kenlay, Frédéric A. Dreyer, Aleksandr Kovaltsuk
et al.
Antibodies are proteins produced by the immune system that can identify and neutralise a wide variety of antigens with high specificity and affinity, and constitute the most successful class of biotherapeutics. With the advent of next-generation sequencing, billions of antibody sequences have been collected in recent years, though their application in the design of better therapeutics has been constrained by the sheer volume and complexity of the data. To address this challenge, we present IgBert and IgT5, the best performing antibody-specific language models developed to date which can consistently handle both paired and unpaired variable region sequences as input. These models are trained comprehensively using the more than two billion unpaired sequences and two million paired sequences of light and heavy chains present in the Observed Antibody Space dataset. We show that our models outperform existing antibody and protein language models on a diverse range of design and regression tasks relevant to antibody engineering. This advancement marks a significant leap forward in leveraging machine learning, large scale data sets and high-performance computing for enhancing antibody design for therapeutic development.
Text-based foundation models have become an important part of scientific discovery, with molecular foundation models accelerating advancements in material science and molecular design.However, existing models are constrained by closed-vocabulary tokenizers that capture only a fraction of molecular space. In this work, we systematically evaluate 34 tokenizers, including 19 chemistry-specific ones, and reveal significant gaps in their coverage of the SMILES molecular representation. To assess the impact of tokenizer choice, we introduce n-gram language models as a low-cost proxy and validate their effectiveness by pretraining and finetuning 18 RoBERTa-style encoders for molecular property prediction. To overcome the limitations of existing tokenizers, we propose two new tokenizers -- Smirk and Smirk-GPE -- with full coverage of the OpenSMILES specification. The proposed tokenizers systematically integrate nuclear, electronic, and geometric degrees of freedom; facilitating applications in pharmacology, agriculture, biology, and energy storage. Our results highlight the need for open-vocabulary modeling and chemically diverse benchmarks in cheminformatics.
Farzin Zobdeh, Aziza Kraiem, Misty M. Attwood
et al.
Migraine is the sixth most prevalent disease globally, a major cause of disability, and it imposes an enormous personal and socio‐economic burden. Migraine treatment is often limited by insufficient therapy response, leading to the need for individually adjusted treatment. In this review, we analyse historical and current pharmaceutical development approaches in acute and chronic migraine based on a comprehensive and systematic analysis of Food and Drug Administration (FDA)‐approved drugs and those under investigation. The development of migraine therapeutics has significantly intensified during the last 3 years, as shown by our analysis of the trends of drug development between 1970 and 2020. The spectrum of drug targets has expanded considerably, which has been accompanied by an increase in the number of specialised clinical trials. This review highlights the mechanistic implications of FDA‐approved and currently investigated drugs and discusses current and future therapeutic options based on identified drug classes of interest.
Osteoarthritis (OA) is the leading cause of function loss and disability among the elderly, with significant burden on the individual and society. It is a severe disease for its high disability rates, morbidity, costs, and increased mortality. Multifactorial etiologies contribute to the occurrence and development of OA. The heterogeneous condition poses a challenge for the development of effective treatment for OA; however, emerging treatments are promising to bring benefits for OA management in the future. This narrative review will discuss recent developments of agents for the treatment of OA, including potential disease-modifying osteoarthritis drugs (DMOADs) and novel therapeutics for pain relief. This review will focus more on drugs that have been in clinical trials, as well as attractive drugs with potential applications in preclinical research. In the past few years, it has been realized that a complex interaction of multifactorial mechanisms is involved in the pathophysiology of OA. The authors believe there is no miracle therapeutic strategy fitting for all patients. OA phenotyping would be helpful for therapy selection. A variety of potential therapeutics targeting inflammation mechanisms, cellular senescence, cartilage metabolism, subchondral bone remodeling, and the peripheral nociceptive pathways are expected to reshape the landscape of OA treatment over the next few years. Precise randomized controlled trials (RCTs) are expected to identify the safety and efficacy of novel therapies targeting specific mechanisms in OA patients with specific phenotypes.
Carla Floricel, Andrew Wentzel, Abdallah Mohamed
et al.
Personalized head and neck cancer therapeutics have greatly improved survival rates for patients, but are often leading to understudied long-lasting symptoms which affect quality of life. Sequential rule mining (SRM) is a promising unsupervised machine learning method for predicting longitudinal patterns in temporal data which, however, can output many repetitive patterns that are difficult to interpret without the assistance of visual analytics. We present a data-driven, human-machine analysis visual system developed in collaboration with SRM model builders in cancer symptom research, which facilitates mechanistic knowledge discovery in large scale, multivariate cohort symptom data. Our system supports multivariate predictive modeling of post-treatment symptoms based on during-treatment symptoms. It supports this goal through an SRM, clustering, and aggregation back end, and a custom front end to help develop and tune the predictive models. The system also explains the resulting predictions in the context of therapeutic decisions typical in personalized care delivery. We evaluate the resulting models and system with an interdisciplinary group of modelers and head and neck oncology researchers. The results demonstrate that our system effectively supports clinical and symptom research.
Shabana Siddique, Imen Farhat, Cariton Kubwabo
et al.
Exposure to organophosphate esters (OPEs) is extensive, yet few studies have investigated their association with hormone levels or semen quality. Here, we studied the association between urinary concentrations of OPEs and their metabolites with hormone levels and semen parameters in men (n = 117) predominantly in the 20–29 years age range who were recruited from the greater Montreal area between 2009 and 2012. Urine, serum, and semen samples were analyzed for OPEs, hormones, and semen quality, respectively. Bis(2-ethylhexyl) phosphate (BEHP), bis(2,4-di-tert-butylphenyl) hydrogen phosphate (B2,4DtBPP), tris(2-chloroisopropyl) phosphate (TCIPP), diphenyl phosphate (DPHP), bis (2-butoxyethyl) phosphate (BBOEP) and di-cresyl phosphate (DCPs) were detected in urine at a frequency ≥ 95%. The highest geometric mean concentration was observed for DPHP (18.54 ng/mL) and the second highest was B2,4DtBPP (6.23 ng/mL). Associations between a doubling in analyte concentrations in urine and hormone levels and semen quality parameters were estimated using multivariable linear regression. B2,4DtBPP levels were positively associated with total T3 (β = 0.09; 95% CI: 0.01, 0.17). DPHP was inversely associated with estradiol (β = -2.56; 95% CI: −5.00, −0.17), and TCIPP was inversely associated with testosterone (β = -0.78; 95% CI: −1.40, −0.17). Concentrations of BCIPP were inversely associated with sperm concentrations (β = -7.76; 95% CI: −14.40, −0.61), progressive motility (β = − 4.98; 95% CI: −8.71, −1.09), and the sperm motility index (β = -9.72; 95% CI: −17.71, −0.96). In contrast, urinary DPHP concentrations were positively associated with the sperm motility (β = 4.37; 95% CI: 0.76, 8.12) and fertility indices (β = 6.64; 95% CI: 1.96, 11.53). Thus, OPE detection rates were high and exposure to several OPEs was associated with altered hormone levels and semen parameters. The possibility that OPEs affect male reproduction warrants further investigation.
The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are important in drug discovery as they define efficacy and safety. In this work, we applied an ensemble of features, including fingerprints and descriptors, and a tree-based machine learning model, extreme gradient boosting, for accurate ADMET prediction. Our model performs well in the Therapeutics Data Commons ADMET benchmark group. For 22 tasks, our model is ranked first in 18 tasks and top 3 in 21 tasks. The trained machine learning models are integrated in ADMETboost, a web server that is publicly available at https://ai-druglab.smu.edu/admet.
Luciana Melina Luque, Carlos Manuel Carlevaro, Enrique Rodríguez-Lomba
et al.
One of the barriers to the development of effective adoptive cell transfer therapies (ACT), specifically for genetically engineered T-cell receptors (TCRs), and chimeric antigen receptor (CAR) T-cells, is target antigen heterogeneity. It is thought that intratumor heterogeneity is one of the leading determinants of therapeutic resistance and treatment failure. While understanding antigen heterogeneity is important for effective therapeutics, a good therapy strategy could enhance the therapy efficiency. In this work we introduce an agent-based model to rationalize the outcomes of two types of ACT therapies over heterogeneous tumors: antigen specific ACT therapy and multi-antigen recognition ACT therapy. We found that one dose of antigen specific ACT therapy should be expected to reduce the tumor size as well as its growth rate, however it may not be enough to completely eliminate it. A second dose also reduced the tumor size as well as the tumor growth rate, but, due to the intratumor heterogeneity, it turned out to be less effective than the previous dose. Moreover, an interesting emergent phenomenon results from the simulations, namely the formation of a shield-like structure of cells with low oncoprotein expression. This shield turns out to protect cells with high oncoprotein expression. On the other hand, our studies suggest that the earlier the multi-antigen recognition ACT therapy is applied, the more efficient it turns. In fact, it could completely eliminate the tumor. Based on our results, it is clear that a proper therapeutic strategy could enhance the therapies outcomes. In that direction, our computational approach provides a framework to model treatment combinations in different scenarios and explore the characteristics of successful and unsuccessful treatments.
Marine natural products (MNPs) have been an important and rich source for antimicrobial drug discovery and an effective alternative to control drug resistant infections. Herein, we report bioassay guided fractionation of marine extracts from sponges <i>Lendenfeldia</i>, <i>Ircinia</i> and <i>Dysidea</i> that led us to identify novel compounds with antimicrobial properties. Tertiary amines or quaternary amine salts: aniline <b>1</b>, benzylamine <b>2</b>, tertiary amine <b>3</b> and <b>4</b>, and quaternary amine salt <b>5</b>, along with three known compounds (<b>6–8</b>) were isolated from a crude extract and MeOH eluent marine extracts. The antibiotic activities of the compounds, and their isolation as natural products have not been reported before. Using tandem mass spectrometry (MS) analysis, potential structures of the bioactive fractions were assigned, leading to the hit validation of potential compounds through synthesis, and commercially available compounds. This method is a novel strategy to overcome insufficient quantities of pure material (NPs) for drug discovery and development which is a big challenge for pharmaceutical companies. The antibacterial screening of the marine extracts has shown several of the compounds exhibited potent in-vitro antibacterial activity, especially against methicillin-resistant <i>Staphylococcus aureus</i> (MRSA) with minimum inhibitory concentration (MIC) values between 15.6 to 62.5 microg mL<sup>−1</sup>. Herein, we also report structure activity relationships of a diverse range of commercial structurally similar compounds. The structure-activity relationships (SAR) results demonstrate that modification of the amines through linear chain length, and inclusion of aromatic rings, modifies the observed antimicrobial activity. Several commercially available compounds, which are structurally related to the discovered molecules, showed broad-spectrum antimicrobial activity against different test pathogens with a MIC range of 50 to 0.01 µM. The results of cross-referencing antimicrobial activity and cytotoxicity establish that these compounds are promising potential molecules, with a favourable therapeutic index for antimicrobial drug development. Additionally, the SAR studies show that simplified analogues of the isolated compounds have increased bioactivity.