Hasil untuk "Therapeutics. Pharmacology"

Erik Hartman, Di Tang, Johan Malmström

Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of \textit{Streptococcus pneumoniae}. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \href{https://github.com/ErikHartman/bopep}{GitHub}.

Jared Moore, Declan Grabb, William Agnew et al.

Should a large language model (LLM) be used as a therapist? In this paper, we investigate the use of LLMs to *replace* mental health providers, a use case promoted in the tech startup and research space. We conduct a mapping review of therapy guides used by major medical institutions to identify crucial aspects of therapeutic relationships, such as the importance of a therapeutic alliance between therapist and client. We then assess the ability of LLMs to reproduce and adhere to these aspects of therapeutic relationships by conducting several experiments investigating the responses of current LLMs, such as `gpt-4o`. Contrary to best practices in the medical community, LLMs 1) express stigma toward those with mental health conditions and 2) respond inappropriately to certain common (and critical) conditions in naturalistic therapy settings -- e.g., LLMs encourage clients' delusional thinking, likely due to their sycophancy. This occurs even with larger and newer LLMs, indicating that current safety practices may not address these gaps. Furthermore, we note foundational and practical barriers to the adoption of LLMs as therapists, such as that a therapeutic alliance requires human characteristics (e.g., identity and stakes). For these reasons, we conclude that LLMs should not replace therapists, and we discuss alternative roles for LLMs in clinical therapy.

Shaoqing Chen, Zhou Fang, Zheng Hu et al.

Investigating the dynamics of growing cell populations is crucial for unraveling key biological mechanisms in living organisms, with many important applications in therapeutics and biochemical engineering. Classical agent-based simulation algorithms are often inefficient for these systems because they track each individual cell, making them impractical for fast (or even exponentially) growing cell populations. To address this challenge, we introduce a novel stochastic simulation approach based on a Feynman-Kac-like representation of the population dynamics. This method, named the Feynman-Kac-inspired Gillespie's Stochastic Simulation Algorithm (FKG-SSA), always employs a fixed number of independently simulated cells for Monte Carlo computation of the system, resulting in a constant computational complexity regardless of the population size. Furthermore, we theoretically show the statistical consistency of the proposed method, indicating its accuracy and reliability. Finally, a couple of biologically relevant numerical examples are presented to illustrate the approach. Overall, the proposed FKG-SSA effectively addresses the challenge of simulating growing cell populations, providing a solid foundation for better analysis of these systems.

Ekaterina Podplutova, Anastasia Vepreva, Olga A. Konovalova et al.

The integration of artificial intelligence (AI) in early-stage drug discovery offers unprecedented opportunities for exploring chemical space and accelerating hit-to-lead optimization. However, docking optimization in generative approaches is computationally expensive and may lead to inaccurate results. Here, we present a novel generative framework that balances pharmacophore similarity to reference compounds with structural diversity from active molecules. The framework allows users to provide custom reference sets, including FDA-approved drugs or clinical candidates, and guides the \textit{de novo} generation of potential therapeutics. We demonstrate its applicability through a case study targeting estrogen receptor modulators and antagonists for breast cancer. The generated compounds maintain high pharmacophoric fidelity to known active molecules while introducing substantial structural novelty, suggesting strong potential for functional innovation and patentability. Comprehensive evaluation of the generated molecules against common drug-like properties confirms the robustness and pharmaceutical relevance of the approach.

Latent Labs Team, Henry Kenlay, Daniella Pretorius et al.

Drug discovery has long sought computational systems capable of designing drug-like molecules directly: developable and non-immunogenic from the start. Here we introduce Latent-X2, a frontier generative model that achieves this goal through zero-shot design of antibodies with strong binding affinities, drug-like properties, and, for the first time for any de novo generated antibody, confirmed low immunogenicity in human donor panels. Latent-X2 is an all-atom model conditioned on target structure, epitope specification, and optional antibody framework, jointly generating sequences and structures while modelling the bound complex. Testing only 4 to 24 designs per target in each modality, we successfully generated VHH and scFv antibodies against 9 of 18 evaluated targets, achieving a 50% target-level success rate with picomolar to nanomolar binding affinities. Designed molecules exhibit developability profiles that match or exceed those of approved antibody therapeutics, including expression yield, aggregation propensity, polyreactivity, hydrophobicity, and thermal stability, without optimization, filtering, or selection. In the first immunogenicity assessment of any AI-generated antibody, representative de novo VHH binders targeting TNFL9 exhibit both potent target engagement and low immunogenicity across T-cell proliferation and cytokine release assays. The model generalizes beyond antibodies: against K-Ras, long considered undruggable, we generated macrocyclic peptide binders competitive with trillion-scale mRNA display screens. These properties emerge directly from the model, demonstrating the therapeutic viability of zero-shot molecular design, now available without AI infrastructure or coding expertise at https://platform.latentlabs.com.

Alina Nussbaumer-Pröll, Bela Hausmann, Maria Weber et al.

<b>Background</b>: The collection of microorganisms that colonize the human genital and urinary tract is referred to as the genitourinary microbiome. Urinary tract infections (UTIs), which predominantly affect women, are linked to alterations in the genitourinary microbiome. Cranberries (<i>Vaccinium oxycoccos</i>), rich in proanthocyanidins, and ascorbic acid (vitamin C), known for their urinary acidification properties, are commonly used for UTI prevention. However, their effects on the genitourinary microbiome remain inadequately characterized. This pilot study assesses the genitourinary microbiome composition in healthy women and evaluates the influence of cranberry and ascorbic acid supplementation. <b>Methods</b>: In a randomized, controlled, and open-label trial, 27 healthy women in their reproductive age (18–40 years) were assigned to three groups: cranberry (n = 8), ascorbic acid (n = 10), and control (n = 9). Urine samples were collected at three time points and processed for 16S rRNA gene amplicon-based microbial community composition analysis. Microbiome composition was compared within and between groups, and between study visits. <b>Results</b>: Sufficient microbial DNA was extracted from all midstream urine samples. The genitourinary microbiome was predominantly composed of <i>Lactobacillus</i> spp., as reported previously. No significant shifts in microbial composition were observed in response to cranberry or ascorbic acid supplementation, and no statistically significant differences were detected between the intervention and control groups or between study visits. <b>Conclusion</b>: The genitourinary microbiome of healthy women remained stable during cranberry or ascorbic acid supplementation. Further studies in patients with recurrent UTIs are needed to explore the potential impacts of these supplements on the genitourinary microbiome in disease states.

B. Blanco, R. Palma, M. Hurtado et al.

Targeted therapeutic interventions utilizing low-inten\-sity ultrasound (LIUS) exhibit substantial potential for hindering the proliferation of cancer stem cells. This investigation introduces a multiscale model and computational framework to comprehensively explore the therapeutic LIUS on poroelastic tumor dynamics, thereby unraveling the intricacies of mechanotransduction mechanisms at play. Our model includes both macroscopic timescales encompassing days and rapid timescales spanning from microseconds to seconds, facilitating an in-depth comprehension of tumor behavior. We unveil the discerning suppression or reorientation of cancer cell proliferation and migration, enhancing a notable redistribution of cellular phases and stresses within the tumor microenvironment. Our findings defy existing paradigms by elucidating the impact of LIUS on cancer stem cell behavior. This endeavor advances our fundamental understanding of mechanotransduction phenomena in the context of LIUS therapy, thus underscoring its promising as a targeted therapeutic modality for cancer treatment. Furthermore, our results make a substantial contribution to the broader scientific community by shedding light on the intricate interplay between mechanical forces, cellular responses, and the spatiotemporal evolution of tumors. These insights hold the promising to promote a new perspective for the future development of pioneering and highly efficacious therapeutic strategies for combating cancer in a personalized manner.

Jie Gao, Jing Hu, Lihang Liu et al.

The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate molecular interactions that underlie immune responses. Despite recent progress with deep learning models like AlphaFold and RoseTTAFold, accurately modeling antigen-antibody complexes remains a challenge due to their unique evolutionary characteristics. HelixFold-Multimer, a specialized model developed for this purpose, builds on the framework of AlphaFold-Multimer and demonstrates improved precision for antigen-antibody structures. HelixFold-Multimer not only surpasses other models in accuracy but also provides essential insights into antibody development, enabling more precise identification of binding sites, improved interaction prediction, and enhanced design of therapeutic antibodies. These advances underscore HelixFold-Multimer's potential in supporting antibody research and therapeutic innovation.

Yunlong Yang, Ying Yuan

The primary objective of Phase I oncology trials is to assess the safety and tolerability of novel therapeutics. Conventional dose escalation methods identify the maximum tolerated dose (MTD) based on dose-limiting toxicity (DLT). However, as cancer therapies have evolved from chemotherapy to targeted therapies, these traditional methods have become problematic. Many targeted therapies rarely produce DLT and are administered over multiple cycles, potentially resulting in the accumulation of lower-grade toxicities, which can lead to intolerance, such as dose reduction or interruption. To address this issue, we proposed dual-criterion designs that find the MTD based on both DLT and non-DLT-caused intolerance. We considered the model-based design and model-assisted design that allow real-time decision-making in the presence of pending data due to long event assessment windows. Compared to DLT-based methods, our approaches exhibit superior operating characteristics when intolerance is the primary driver for determining the MTD and comparable operating characteristics when DLT is the primary driver.

Dan Su, Qianmin Li, Xin Lai et al.

Immune inflammation is one of the main factors in the pathogenesis of depression. It is an effective and active way to find more safe and effective anti-inflammatory depressant drugs from plant drugs. The purpose of this study is to explore the potential of marine plant Sargassum pallidum (Turn).C.Ag. (Haihaozi, HHZ) in the prevention and treatment of depression and to explain the related mechanism. Phytochemical analysis showed that alkaloids, terpenes, and organic acids are the main constituents. In vitro and in vivo activity studies showed the anti-neuroinflammatory and antidepressant effect of Sargassum pallidum, furthermore, confirmed that 7-Hydroxycoumarin, Scoparone, and Kaurenoic Acid are important plant metabolites in Sargasum pallidum for anti-neuroinflammation. Mechanism exploration showed that inhibition of ERK1/2/p38 inflammatory signaling pathway contributing to the antidepressant effect of Sargassum pallidum in reducing intestinal inflammatory levels. This study confirmed the value of Sargassum pallidum and its rich plant metabolites in anti-inflammatory depression, providing a new choice for the follow-up research and development of antidepressant drugs.

Feigni Youyi Marcelle Olga, Mbiantcha Marius, Yousseu Nana William et al.

Cissus quadrangularis Linn. (C. quadrangularis, Vitaceae) is a plant reported to treat injured tendons, broken bones, asthma, stomach ache, scurvy, and digestive disorders. The present study evaluated the antihyperalgesic effects of ethanolic extract of C. quadrangularis Linn. Vincristine sulfate (100 μg/kg, i.p.) was administered in rats for 10 days with 2 days break to induce painful peripheral neuropathy. Mechanical hyperalgesia and allodynia tests were performed to assess the threshold of painful neuropathy. Calcium levels in the sciatic nerve, oxidant stress markers, and levels of GABA and 5-HT were also determined in the brain and spinal cord after 15 days. Ethanolic extract of C. quadrangularis (180 and 360 mg/kg) and pregabalin (50 mg/kg) were administered for 15 consecutive days. The results revealed that the extract significantly (p<0.001) inhibited hyperalgesia and allodynia in animals after vincristine administration. The extract decreased total calcium levels in the sciatic nerve, MDA levels while increasing GSH activity, 5-HT level, as well as GABA levels in the brain and spinal cord. The results of this study suggest that the ethanolic extract of C. quadrangularis uses antioxidant capacity, calcium inhibitory action, and neuromodulation of GABA and 5-HT to prevent the development of painful neuropathy after vincristine administration. This demonstrates that C. quadrangularis is a promising molecule for the management of peripheral neuropathic pain induced by anticancer drugs.

Wanting Su, Dongwei Liu, Feng Tan et al.

There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.

Fatema A. Alqenae, Douglas Steinke, Andrew Carson-Stevens et al.

Introduction: Improving medication safety during transition of care is an international healthcare priority. While existing research reveals that medication-related incidents and associated harms may be common following hospital discharge, there is limited information about their nature and contributory factors at a national level which is crucial to inform improvement strategy. Aim: To characterise the nature and contributory factors of medication-related incidents during transition of care from secondary to primary care. Method: A retrospective analysis of medication incidents reported to the National Reporting and Learning System (NRLS) in England and Wales between 2015 and 2019. Descriptive analysis identified the frequency and nature of incidents and content analysis of free text data, coded using the Patient Safety Research Group (PISA) classification, examined the contributory factors and outcome of incidents. Results: A total of 1121 medication-related incident reports underwent analysis. Most incidents involved patients over 65 years old (55%, n  = 626/1121). More than one in 10 (12.6%, n  = 142/1121) incidents were associated with patient harm. The drug monitoring (17%) and administration stages (15%) were associated with a higher proportion of harmful incidents than any other drug use stages. Common medication classes associated with incidents were the cardiovascular ( n  = 734) and central nervous ( n  = 273) systems. Among 408 incidents reporting 467 contributory factors, the most common contributory factors were organisation factors (82%, n  = 383/467) (mostly related to continuity of care which is the delivery of a seamless service through integration, co-ordination, and the sharing of information between different providers), followed by staff factors (16%, n  = 75/467). Conclusion: Medication incidents after hospital discharge are associated with patient harm. Several targets were identified for future research that could support the development of remedial interventions, including commonly observed medication classes, older adults, increase patient engagement, and improve shared care agreement for medication monitoring post hospital discharge. Plain language summary Study using reports about unsafe or substandard care mainly written by healthcare professionals to better understand the type and causes of medication safety problems following hospital discharge Why was the study done? The safe use of medicines after hospital discharge has been highlighted by the World Health Organization as an important target for improvement in patient care. Yet, the type of medication problems which occur, and their causes are poorly understood across England and Wales, which may hamper our efforts to create ways to improve care as they may not be based on what we know causes the problem in the first place. What did the researchers do? The research team studied medication safety incident reports collected across England and Wales over a 5-year period to better understand what kind of medication safety problems occur after hospital discharge and why they happen, so we can find ways to prevent them from happening in future. What did the researchers find? The total number of incident reports studied was 1121, and the majority ( n  = 626) involved older people. More than one in ten of these incidents caused harm to patients. The most common medications involved in the medication safety incidents were for cardiovascular diseases such as high blood pressure, conditions such as mental illness, pain and neurological conditions (e.g., epilepsy) and other illnesses such as diabetes. The most common causes of these incidents were because of the organisation rules, such as information sharing, followed by staff issues, such as not following protocols, individual mistakes and not having the right skills for the task. What do the findings mean? This study has identified some important targets that can be a focus of future efforts to improve the safe use of medicines after hospital discharge. These include concentrating attention on medication for the cardiovascular and central nervous systems (e.g., via incorporating them in prescribing safety indicators and pharmaceutical prioritisation tools), staff skill mix (e.g., embedding clinical pharmacist roles at key parts of the care pathway where greatest risk is suspected), and implementation of electronic interventions to improve timely communication of medication and other information between healthcare providers.

Takuaki Tani, Shinobu Imai, Kiyohide Fushimi

Objective: To analyse the association between the daily duration of rehabilitation for inpatients with sporadic inclusion body myositis and improvement in activities of daily living, using a Japanese nationwide inpatient administrative claims database. Methods: Data were extracted regarding inpatients with sporadic inclusion body myositis who had undergone rehabilitation between 1 April 2018 and 31 March 2021. The mean daily duration of rehabilitation was categorized into 2 groups: > 1.0 h (longer rehabilitation) and ≤ 1.0 h (shorter rehabilitation). The main outcome was improvement in activities of daily living from admission to discharge, measured using the Barthel Index. For the main analysis, a generalized linear model was used. Results: In total, 424 patients with sporadic inclusion body myositis met the eligibility criteria for inclusion in the study. The main analysis found a significant difference in improvement in activities of daily living between the longer rehabilitation and shorter rehabilitation groups after adjusting for confounders (risk ratio (95% confidence interval), 1.37 (1.06–1.78)). Conclusion: A longer daily duration of rehabilitation results in improved activities of daily living for inpatients with sporadic inclusion body myositis. LAY ABSTRACT Sporadic inclusion body myositis is a slowly progressive inflammatory myopathy. There is no known effective systemic therapy for sporadic inclusion body myositis; hence rehabilitation plays an important role in standard care for most patients. Although rehabilitation is currently provided to inpatients with the condition, there is almost no evidence for an association between the daily duration of rehabilitation and improvement in activities of daily living. The aim of this study was to evaluate the association between the daily duration of rehabilitation for inpatients with sporadic inclusion body myositis and improvement in activities of daily living, using a nationwide administrative database in Japan. The results show that a longer daily duration of rehabilitation results in improved activities of daily living for inpatients with sporadic inclusion body myositis.

Fan Liu, Fan Liu, Hanxi Yi et al.

The in vivo performances of most drugs after extravascular administration are fitted well with the two-compartment pharmacokinetic (PK) model, but the estimation of absorption rate constant (ka) for these drugs becomes difficult during unavailability of intravenous PK data. Herein, we developed a novel method, called the direct method, for estimating the ka values of drugs without using intravenous PK data, by proposing a new PK parameter, namely, maximum apparent rate constant of disposition (kmax). The accuracy of the direct method in ka estimation was determined using the setting parameters (k12, k21, and k10 values at high, medium, and low levels, respectively) and clinical data. The results showed that the absolute relative error of ka estimated using the direct method was significantly lower than that obtained using both the Loo-Riegelman method and the statistical moment method for the setting parameters. Human PK studies of telmisartan, candesartan cilexetil, and tenofovir disoproxil fumarate indicated that the ka values of these drugs were accurately estimated using the direct method based on good correlations between the ka values and other PK parameters that reflected the absorption properties of drugs in vivo (Tmax, Cmax, and Cmax/AUC0-t). This novel method can be applied in situations where intravenous PK data cannot be obtained and is expected to provide valuable support for PK evaluation and in vitro-in vivo correlation establishment.

Lingyue Yan, Yafei Su, Isaac Hsia et al.

Idiopathic pulmonary fibrosis (IPF) is a chronic lung disorder with a low survival rate. Pulmonary fibrosis is one of the complications of COVID-19 and has a high prevalence in COVID-19 patients. Currently, no effective therapies other than lung transplantation are available to cure IPF and post-COVID-19 pulmonary fibrosis. MicroRNAs are small non-coding RNAs that mediate the development and progression of pulmonary fibrosis, thus making them potent drug candidates for this serious disease. MicroRNA-21 (miR-21) promotes not only the differentiation of fibroblasts to myofibroblasts but also epithelial-mesenchymal transition, both of which have been proposed as fundamental processes in pulmonary fibrosis development. Delivery of anti-miR-21 to block the miR-21-associated fibrogenic pathways represents a promising therapy for pulmonary fibrosis. However, microRNA treatment is challenged by quick degradation of RNA in blood, poor cellular uptake, and off-target effects. To overcome these challenges, we developed a lung-targeted, cationic liposome formulation to encapsulate anti-miR-21, enhance its delivery efficiency, and improve the therapeutic efficacy. We optimized the liposome formulation and demonstrated the anti-fibrotic effects using both in vitro and in vivo lung fibrosis models. Our results showed that anti-miR-21 delivered by cationic liposomes suppressed myofibroblast differentiation, reduced the synthesis of extracellular matrix, and inhibited fibrosis progression.

Kieran Didi, Matej Zečević

Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.

Aysen Degerli, Fahad Sohrab, Serkan Kiranyaz et al.

Myocardial infarction (MI) is the leading cause of mortality and morbidity in the world. Early therapeutics of MI can ensure the prevention of further myocardial necrosis. Echocardiography is the fundamental imaging technique that can reveal the earliest sign of MI. However, the scarcity of echocardiographic datasets for the MI detection is the major issue for training data-driven classification algorithms. In this study, we propose a framework for early detection of MI over multi-view echocardiography that leverages one-class classification (OCC) techniques. The OCC techniques are used to train a model for detecting a specific target class using instances from that particular category only. We investigated the usage of uni-modal and multi-modal one-class classification techniques in the proposed framework using the HMC-QU dataset that includes apical 4-chamber (A4C) and apical 2-chamber (A2C) views in a total of 260 echocardiography recordings. Experimental results show that the multi-modal approach achieves a sensitivity level of 85.23% and F1-Score of 80.21%.

C. Courtès, E. Franck, K. Lutz et al.

Modelling epidemics via classical population-based models suffers from shortcomings that so-called individual-based models are able to overcome, as they are able to take heterogeneity features into account, such as super-spreaders, and describe the dynamics involved in small clusters. In return, such models often involve large graphs which are expensive to simulate and difficult to optimize, both in theory and in practice. By combining the reinforcement learning philosophy with reduced models, we propose a numerical approach to determine optimal health policies for a stochastic epidemiological graph-model taking into account super-spreaders. More precisely, we introduce a deterministic reduced population-based model involving a neural network, and use it to derive optimal health policies through an optimal control approach. It is meant to faithfully mimic the local dynamics of the original, more complex, graph-model. Roughly speaking, this is achieved by sequentially training the network until an optimal control strategy for the corresponding reduced model manages to equally well contain the epidemic when simulated on the graph-model. After describing the practical implementation of this approach, we will discuss the range of applicability of the reduced model and to what extent the estimated control strategies could provide useful qualitative information to health authorities.

Chung Yuen Khew, Rahmad Akbar, Norfarhan Mohd. Assaad

Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.