Analysis of molecular mechanisms of drug resistance of Mycobacterium tuberculosis in patients with pulmonary tuberculosis and its pharmacoeconomics

Purpose: To investigate the molecular mechanisms of drug resistance of Mycobacterium tuberculosis in patients with pulmonary tuberculosis and its pharmacoeconomics. Methods: Data pertaining to patients with primary tuberculosis treated in the First Affiliated Hospital of Zhaoqing Medical College, Zhaoqing, China from January 2020 to June 2021 were retrospectively analyzed. Sputum specimens were collected from all eligible patients, and 151 uncontaminated specimens with good bacteriophage activity were screened. Results: A total of 107 Mycobacterium tuberculosis strains were isolated from the 151 specimens, 31 of which strains were resistant to varying degrees to rifampicin, isoniazid, streptomycin, and ethambutol with an overall resistance of 28.97 %. There were 16 strains with rpoB mutation, 22 strains with katG mutation, and 8 strains with inhA mutation. The difference in the sputum negative rate, lesion absorption rate, and tuberculosis cavity closure rate, and total medical cost between the two group were not statistically significant (p > 0.05). The incidence of adverse reactions in the FDC group was significantly lower than that in the blister pack group (p < 0.05). Conclusion: The total resistance of Mycobacterium tuberculosis in primary tuberculosis patients remains at a high level, and the development of resistance is associated with mutations in rpoB, katG, and inhA genes. FDC regimen provides more pharmacoeconomic and therapeutic benefits than blister pack regimen

A novel 25-ferroptosis-related gene signature for the prognosis of gliomas

BackgroundFerroptosis is closely associated with cancer and is of great importance in the immune evasion of cancer. However, the relationship between ferroptosis and glioma is unclear.MethodsWe downloaded the expression profiles and clinical data of glioma from the GlioVis database and obtained the expression profiles of ferroptosis genes. A ferroptosis-related gene signature was developed for the prognosis of gliomas.ResultsWe screened out prognostic ferroptosis genes, named ferroptosis-related genes, by the Cox regression method. Based on these genes, we used unsupervised clustering to obtain two different clusters; the principal component analysis algorithm was applied to determine the gene score of each patient, and then all the patients were classified into two subgroups. Results showed that there exist obvious differences in survival between different clusters and different gene score subgroups. The prognostic model constructed by the 25 ferroptosis-related genes was then evaluated to predict the clinicopathological features of immune activity in gliomas.ConclusionThe ferroptosis-related genes play an important role in the malignant process of gliomas, potentially contributing to the development of prognostic stratification and treatment strategies

Self-Driving Laboratories: A Paradigm Shift in Nanomedicine Development

Nanomedicines have transformed promising therapeutic agents into clinically approved medicines with optimal safety and efficacy profiles. This is exemplified by the mRNA vaccines against COVID-19, which were made possible by lipid nanoparticle technology. Despite the success of nanomedicines to date, their design remains far from trivial in part due to the complexity associated with their preclinical development. Herein we propose a nanomedicine materials acceleration platform (NanoMAP) to streamline the preclinical development of these formulations. NanoMAP combines high-throughput experimentation with state-of-the-art advances in artificial intelligence (including active learning and few-shot learning) as well as a web-based application for data sharing. The deployment of NanoMAP requires interdisciplinary collaboration between leading figures in drug delivery and artificial intelligence to enable this data-driven design approach. The proposed approach will not only expedite the development of next generation nanomedicines, but also encourage participation of the pharmaceutical science community in a large data curation initiative

Data-driven development of an oral lipid-based nanoparticle formulation of a hydrophobic drug

Due to its cost-effectiveness, convenience, and high patient adherence, oral drug administration often remains the preferred approach. Yet, the effective delivery of hydrophobic drugs via the oral route is often hindered by their limited water solubility and first-pass metabolism. To mitigate these challenges, advanced delivery systems such as solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) have been developed to encapsulate hydrophobic drugs and enhance their bioavailability. However, traditional design methodologies for these complex formulations often present intricate challenges because they are restricted to a relatively narrow design space. Here, we present a data-driven approach for the accelerated design of SLNs/NLCs encapsulating a model hydrophobic drug, cannabidiol, that combines experimental automation and machine learning. A small subset of formulations, comprising 10% of all formulations in the design space, was prepared in-house, leveraging miniaturized experimental automation to improve throughput and decrease the quantity of drug and materials required. Machine learning models were then trained on the data generated from these formulations and used to predict properties of all SLNs/NLCs within this design space (i.e., estimated to be more than 1200 formulations). Notably, formulations predicted to be high-performers via this approach were confirmed to significantly enhance the solubility of the drug by up to 3000-fold and prevent drug degradation. Moreover, our high-performance formulations significantly enhanced the oral bioavailability of the drug compared to both its free form and an over-the-counter version. Furthermore, this bioavailability matched that of a formulation equivalent in composition to the FDA-approved product, Epidiolex®

Data evaluating triamcinolone acetonide and triamcinolone hexacetonide loaded poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles

Advanced drug delivery strategies can be used to enhance the therapeutic effectiveness of locally delivered corticosteroids. Poly(δ-valerolactone-co-allyl-δ-valerolactone) microparticles (PVL-co-PAVL MPs) were evaluated for delivery of two corticosteroids, triamcinolone acetonide and triamcinolone hexacetonide. PVL-co-PAVL MPs were prepared using a modified oil-in-water emulsification method, followed by a UV-initiated cross-linking process. The resulting PVL-co-PAVL MPs were purified with an excess amount of water and then acetone to remove residual surfactant, cross-linker, and catalyst before lyophilization. Triamcinolone acetonide and triamcinolone hexacetonide were independently loaded into the resulting PVL-co-PAVL MPs via a post-loading swelling-equilibrium method. The drug-loaded MPs were characterized in terms of drug loading (determined by high-performance liquid chromatography, HPLC), thermal properties (determined by differential scanning calorimetry, DSC), and in vitro drug release kinetics (with quantification of drug using HPLC) to better understand the suitability of PVL-co-PAVL MPs for delivery of corticosteroids. These data demonstrate the potential of PVL-co-PAVL MPs as a promising drug delivery platform for the sustained release of corticosteroids. Raw data have been made available on Mendeley Data. Additional details on PVL-co-PAVL MPs were previously reported [1]

Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables

Long-acting injectables are considered one of the most promising therapeutic strategies for the treatment of chronic diseases as they can afford improved therapeutic efficacy, safety, and patient compliance. The use of polymer materials in such a drug formulation strategy can offer unparalleled diversity owing to the ability to synthesize materials with a wide range of properties. However, the interplay between multiple parameters, including the physicochemical properties of the drug and polymer, make it very difficult to intuitively predict the performance of these systems. This necessitates the development and characterization of a wide array of formulation candidates through extensive and time-consuming in vitro experimentation. Machine learning is enabling leap-step advances in a number of fields including drug discovery and materials science. The current study takes a critical step towards data-driven drug formulation development with an emphasis on long-acting injectables. A series of machine learning algorithms were trained and refined for accurate prediction of experimental drug release profiles. Analysis of the best performing model uncovered the properties of the drug and polymer that were identified to be key determinants of drug release. This information can be used to identify promising drug-polymer combinations that result in long-acting injectables with specific drug release behaviour. The implementation of this data-driven approach has the potential to reduce the time and cost associated with formulation development. Datasets and relevant codes used to train the machine learning models have been made openly available to encourage usage in future drug formulation efforts

Machine learning models to accelerate the design of polymeric long-acting injectables

Polymer-based long-acting injectable drugs are a promising therapeutic strategy for chronic diseases. Here the authors use machine learning to inform the data-driven development of advanced drug formulations

Atlas: A Brain for Self-driving Laboratories

Self-driving laboratories (SDLs) are next-generation research and development platforms for closed-loop, autonomous experimentation that combine ideas from artificial intelligence, robotics, and high-performance computing. A critical component of SDLs is the decision-making algorithm used to prioritize experiments to be performed. This SDL “brain” often relies on optimization strategies that are guided by machine learning models, such as Bayesian optimization. However, the diversity of hardware constraints and scientific questions being tackled by SDLs require the availability of a set of flexible algorithms that have yet to be implemented in a single software tool. Here, we report Atlas, an application-agnostic Python library for Bayesian optimization that is specifically tailored to the needs of SDLs. Atlas provides facile access to state-of-the-art, model-based optimization algorithms—including mixed-parameter, multi-objective, constrained, robust, multi-fidelity, meta-learning, and molecular optimization—as an all-in-one tool that is expected to suit the majority of specialized SDL needs. After a brief description of its core capabilities, we demonstrate Atlas’ utility by optimizing the oxidation potential of metal complexes with an autonomous electrochemical experimentation platform. We expect Atlas to expand the breadth of design and discovery problems in the natural sciences that are immediately addressable with SDLs

DataSheet_1_A novel 25-ferroptosis-related gene signature for the prognosis of gliomas.docx

BackgroundFerroptosis is closely associated with cancer and is of great importance in the immune evasion of cancer. However, the relationship between ferroptosis and glioma is unclear.MethodsWe downloaded the expression profiles and clinical data of glioma from the GlioVis database and obtained the expression profiles of ferroptosis genes. A ferroptosis-related gene signature was developed for the prognosis of gliomas.ResultsWe screened out prognostic ferroptosis genes, named ferroptosis-related genes, by the Cox regression method. Based on these genes, we used unsupervised clustering to obtain two different clusters; the principal component analysis algorithm was applied to determine the gene score of each patient, and then all the patients were classified into two subgroups. Results showed that there exist obvious differences in survival between different clusters and different gene score subgroups. The prognostic model constructed by the 25 ferroptosis-related genes was then evaluated to predict the clinicopathological features of immune activity in gliomas.ConclusionThe ferroptosis-related genes play an important role in the malignant process of gliomas, potentially contributing to the development of prognostic stratification and treatment strategies.</p