Machine learning directed drug formulation development

Advancing cancer gene therapy: the emerging role of nanoparticle delivery systems

Gene therapy holds immense potential due to its ability to precisely target oncogenes, making it a promising strategy for cancer treatment. Advances in genetic science and bioinformatics have expanded the applications of gene delivery technologies beyond detection and diagnosis to potential therapeutic interventions. However, traditional gene therapy faces significant challenges, including limited therapeutic efficacy and the rapid degradation of genetic materials in vivo. To address these limitations, multifunctional nanoparticles have been engineered to encapsulate and protect genetic materials, enhancing their stability and therapeutic effectiveness. Nanoparticles are being extensively explored for their ability to deliver various genetic payloads-including plasmid DNA, messenger RNA, and small interfering RNA-directly to cancer cells. This review highlights key gene modulation strategies such as RNA interference, gene editing systems, and chimeric antigen receptor (CAR) technologies, alongside a diverse array of nanoscale delivery systems composed of polymers, lipids, and inorganic materials. These nanoparticle-based delivery platforms aim to improve targeted transport of genetic material into cancer cells, ultimately enhancing the efficacy of cancer therapies.

Bayesian hierarchical model predicts biopharmaceutical stability indicators and shelf life with application to multivalent human papillomavirus vaccine

Predictive stability is demonstrated as a powerful method for assessing the shelf-life of biopharmaceutical products, such as therapeutic proteins and vaccines. A Bayesian hierarchical multi-level stability model is illustrated for the Human Papillomavirus (HPV) 9-valent recombinant sub-unit vaccine GARDASIL®9. Ensuring speedy manufacturing and ample supply to satisfy the need of patients globally is pivotal, particularly for expanding vaccine access to underserved populations. Product heat stability and cold-chain supply play a major role in deployment of vaccines particularly to lower income countries, while lengthy real-time stability and shelf-life supporting studies are resource-intensive and time-consuming. Hence, an accelerated model-informed stability approach is devised. The product in this case study contains 9 molecular types (antigens) which each target different viral genotypes of the same class of the virus. The molecular types are mixed together as a co-formulation within a container (vial or syringe). The stability behavior of the platform vaccine was well-characterized experimentally and a single stability-limiting attribute was identified (potency). A Bayesian hierarchical stability model was developed utilizing long-term drug product storage data through shelf life at 5 °C as well as shorter-term accelerated stability data at 25 °C and 37 °C for 30 product batches. The model was able to comprehensively assess the stability of all molecular types in the vaccine as well as covariates like container type within a singular unified model framework. Moreover, method superiority was demonstrated for this application over multiple approaches such as linear and mixed effects models. This work elucidates that biopharmaceutical product platform knowledge from previous lots of a biopharmaceutical product in conjunction with batch-specific data from early stability timepoints can be employed to support long-term assessment for shelf-life of the stability and shelf-life indicating attribute(s). These findings, applied to two types vaccines including a multivalent vaccine, hold utility towards enabling accelerated patient access of future complex vaccines and biotherapeutic modalities. The results provide a novel framework for estimating a model for stability data in the context of evolving regulatory guidance.

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

A new era of psoriasis treatment: Drug repurposing through the lens of nanotechnology and machine learning

Psoriasis is a persistent inflammatory skin disorder characterized by hyper-proliferation and abnormal epidermal differentiation. Conventional treatments such as; topical therapies, phototherapy, systemic immune modulators, and biologics aim to relieve symptoms and improve patient quality of life. However, challenges like adverse effects, high costs, and individual response variability persist. Thus, the need for novel anti-psoriatic drugs has led to the exploration of drug repurposing, an approach that identifies new applications for existing drugs. This method is in its early stages but has gained popularity across both public and private sectors. Furthermore, artificial intelligence (AI) integration is revolutionizing the healthcare industry by enhancing efficiency, delivery, and personalization. Machine learning and deep learning algorithms have significantly impacted drug discovery, repurposing, and designing new molecules or drug delivery carriers. Nanotechnology, in addition to AI, plays a pivotal role in targeting repurposed drugs via the topical route with suitable nanocarriers. This method overcomes challenges associated with oral delivery, such as systemic toxicities, slow onset of action, first-pass effect, and poor bioavailability. This review addresses the practice of repurposing existing drugs for managing psoriasis, discussing the challenges of conventional therapy and how the incorporation of nanotechnology and AI can overcome these hurdles, facilitating the discovery of anti-psoriatic drugs and presenting promising strategies for novel therapeutics. Additionally, it discusses the general benefits of drug repurposing compared to de novo drug development and the potential drawbacks of drug repurposing.

Computational Design and Optimization of Multi-Compound Multivesicular Liposomes for Co-Delivery of Traditional Chinese Medicine Compounds

Study explored the synergistic anti-tumor effects of a combination of compounds from Traditional Chinese Medicine, including rosmarinic acid (RA), chlorogenic acid (CA), and scoparone (SCO), in the formulation of multivesicular liposomes (MVLs). Optimization of formulations and process parameters was essential to achieve effective liposomal encapsulation and optimal release profiles for these three compounds with diverse properties. Traditional trial-and-error approaches are inefficient for the optimization of complex multi-compound MVLs. We developed a new formulation optimization model, which could address this issue by predicting the optimal multi-compound MVLs formulation. Our machine learning model integrated support vector machine regression (SVR) algorithm and cuckoo search (CS) algorithm, resulting in three CS-SVR models to predict single-compound MVLs. The CS algorithm, with various weighting rules, was then applied to search the best formulation parameters across three CS-SVR models and to maximize the encapsulation efficiency for all three compounds. The multi-compound MLVs were subsequently prepared under the predicted conditions, achieving an optimized particle size of 15.12 µm, with encapsulation efficiencies of 82.93 ± 2.43% for CA, 82.22 ± 1.25% for RA, and 95.60 ± 0.18% for SCO. The predicted optimal multi-compound MVLs were further validated through in vitro characterization and in vivo anti-tumor experiments, showing a promising synergistic anti-tumor effect consistent with in vitro results. This model accurately predicted optimal encapsulation conditions, which were validated experimentally, demonstrating improved encapsulation efficiencies and reduced trial-and-error iterations. Collectively, our model provides a predictive pathway for multi-compound MVLs formulation, indicating the ability of this model to significantly reduce experimental burden and accelerate formulation development.

AI-directed formulation strategy design initiates rational drug development

Rational drug development would be impossible without selecting the appropriate formulation route. However, pharmaceutical scientists often rely on limited personal experiences to perform trial-and-error tests on diverse formulation strategies. Such an inefficient screening manner not only wastes research investments but also threatens the safety of clinical volunteers and patients. A design-oriented paradigm for formulation strategy determination is urgently needed to initiate rational drug development. Herein, we introduce FormulationDT, the first data-driven and knowledge-guided artificial intelligence (AI) platform for rational formulation strategy design. Learning from approved drug formulations, FormulationDT devised a comprehensive formulation strategy design system containing 12 decisions for both oral and injectable administration. Utilizing PU-Decide, our specialized partially supervised learning framework designed for positive-unlabeled (PU) scenarios, FormulationDT developed precise and interpretable classification models for each decision, achieving area under the receiver operating characteristic curve (ROC_AUC) scores ranging from 0.78 to 0.98, with an average above 0.90. Incorporating extensive domain knowledge, FormulationDT is now accessible through a user-friendly web platform (http://formulationdt.computpharm.org/). Moreover, FormulationDT demonstrates its value by showcasing its application in proteolysis targeting chimeras (PROTACs) and recent drug approvals. Overall, this study created the first approved drug formulation dataset and tailored the PU-Decide framework to develop a high-performance, interpretable, and user-friendly AI formulation strategy design platform, which holds promise for driving risk reduction and efficiency gains across the life cycle of drug discovery and development.

Modelling the effect of base component properties and processing conditions on mixture products using probabilistic, knowledge-guided neural networks

Development of materials by mixing different base components is a widespread methodology to create materials with improved properties compared to those of its base components. However, efficient determination of the properties of mixture-based materials during design remains challenging without prior knowledge of the underlying physical phenomena. In this work a new data-based methodology is proposed involving the use of probabilistic, knowledge-guided artificial neural networks to jointly model the properties of the base components, the proportions in which they are mixed, and the processing conditions used during manufacture to predict properties of final products. The method proposed does not involve any assumptions in terms of ideal mixing rules of the base components, and allows for estimation of aleatoric uncertainty in the prediction. Additionally, an extension is presented that incorporates expert knowledge into the model by the implementation of monotonicity constraints between certain inputs and outputs. The methodology is illustrated with a case study involving the formulation of drug products using direct compression. The model is used to predict pharmaceutical tablets' quality attributes (mass variation, tensile strength, disintegration time, friability and ejection force), showing that the method is able to predict properties of the final product overcoming gaps currently present in previous modelling approaches.

Pharmaceutical Salts: Comprehensive Insights From Fundamental Chemistry to FDA Approvals (2019-2023)

Pharmaceutical salts are a cornerstone in drug development, offering a robust, economical, and industry-friendly option for improving the crucial physicochemical properties of drugs, particularly solubility and dissolution. This review article explores all critical aspects of salt formation, including its importance, the basic chemistry involved, the principles governing counterion selection, the range of counterions used, and the methods for preparing salts along with their advantages and limitations. Additionally, it explores analytical techniques for confirming salt formation and the different approaches various countries adopt in considering new salts as intellectual property. Furthermore, the review sheds light on US FDA-approved salts from 2019 to 2023, providing a unique perspective by analyzing trends in counterion selection observed in FDA-approved salts during this period. Despite the extensive literature on pharmaceutical salts, a comprehensive review addressing all these critical aspects in a single article with a focus on current trends and particularly on US FDA-approved salts from 2019 to 2023 is lacking. This review bridges this gap by thoroughly exploring all mentioned facets of pharmaceutical salts and providing an up-to-date overview.

Machine learning strengthened formulation design of pharmaceutical suspensions

Many different formulation strategies have been investigated to oppose suboptimal treatment of long-term or chronic conditions, one of which are the nano- and microsuspensions prepared as long-acting injectables to prolong the release of an active pharmaceutical compound for a defined period of time by regulating the size of particles by milling. Typically, surfactant and/or polymers are added in the dispersion medium of the suspension during processing for stabilization purposes. However, current formulation investigations with milling are heavily based on prior expertise and trial-and-error approaches. Various interacting parameters such as the milling bead size, stabilizer type and concentration have confounded the investigation of milling process. The present study systematically exploited statistical and machine learning (ML) strategies to understand the relationship between suspension characteristics and formulation parameters under full-factorial milling experiments. Stabilizer concentration was identified as a significant factor (p < 0.001) for median suspension diameter (D50). A formulation stability classification ML model with high prediction accuracy (0.91) and F1-score (0.91) under 10-fold cross-validation was constructed based on 72 formulation datapoints. Model interpretation through Shapley additive explanations (SHAP) revealed the prominent impact of stabilizer concentration and milling bead size on formulation stability. The present work demonstrated the potential to achieve a deeper understanding of the design and optimization of nano- and microsuspensions through explainable ML modelling on formulation screening data.

'Applications of machine learning in liposomal formulation and development'

Machine learning (ML) has emerged as a transformative tool in drug delivery, particularly in the design and optimization of liposomal formulations. This review focuses on the intersection of ML and liposomal technology, highlighting how advanced algorithms are accelerating formulation processes, predicting key parameters, and enabling personalized therapies. ML-driven approaches are restructuring formulation development by optimizing liposome size, stability, and encapsulation efficiency while refining drug release profiles. Additionally, the integration of ML enhances therapeutic outcomes by enabling precision-targeted delivery and minimizing side effects. This review presents current breakthroughs, challenges, and future opportunities in applying ML to liposomal systems, aiming to improve therapeutic efficacy and patient outcomes in various disease treatments.

Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery

Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.

Review of machine learning for lipid nanoparticle formulation and process development

Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes of vaccines, and alternate modes of intracellular delivery for therapeutic proteins. In the pharmaceutical industry, establishing a robust formulation and process to achieve target product performance is a critical component of drug development. Fundamental understanding of the processes for making LNPs and their interactions with biological systems have advanced considerably in the wake of the COVID-19 pandemic. Nevertheless, LNP formulation research remains largely empirical and resource intensive due to the multitude of input parameters and the complex physical phenomena that govern the processes of nanoparticle precipitation, self-assembly, structure evolution, and stability. Increasingly, artificial intelligence and machine learning (AI/ML) are being applied to improve the efficiency of research activities through in silico models and predictions, and to drive deeper fundamental understanding of experimental inputs to functional outputs. This review will identify current challenges and opportunities in the development of robust LNP formulations of nucleic acids, review studies that apply machine learning methods to experimental datasets, and provide discussion on associated data science challenges to facilitate collaboration between formulation and data scientists, aiming to accelerate the advancement of AI/ML applied to LNP formulation and process optimization.

Artificial intelligence-driven pharmaceutical industry: A paradigm shift in drug discovery, formulation development, manufacturing, quality control, and post-market surveillance

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the pharmaceutical industry, ushering in a paradigm shift across various domains, including drug discovery, formulation development, manufacturing, quality control, and post-market surveillance. This comprehensive review examines the multifaceted impact of AI-driven technologies on all stages of the pharmaceutical life cycle. It discusses the application of machine learning algorithms, data analytics, and predictive modeling to accelerate drug discovery processes, optimize formulation development, enhance manufacturing efficiency, ensure stringent quality control measures, and revolutionize post-market surveillance methodologies. By describing the advancements, challenges, and future prospects of harnessing AI in the pharmaceutical landscape, this review offers valuable insights into the evolving dynamics of drug development and regulatory practices in the era of AI-driven innovation.

Unveiling Swelling and Erosion Dynamics: Early Development Screening of Mirabegron Extended Release Tablets

Although the development of extended release (ER) matrices has been extensively investigated, understanding the most appropriate mechanism of drug release to achieve the desired release remains a cost- and time-consuming challenge in the early stages of formulation development. This study aimed to investigate the early stage of developing ER hydrophilic matrix tablets containing mirabegron as a model drug, focusing on the effects of polymer type, diluent type, and polymer amount on critical quality attributes (CQAs), namely, tablet swelling and erosion behavior. A full factorial design was employed to explore the interactions of control factors through multivariate regression analysis, emphasizing the application of quality by design (QbD) principles. The swelling and erosion performances of 72 formulations were evaluated. The swelling data were fitted to the Vergnaud model. Finally, in vitro drug release profiles were investigated for four of the formulations studied. The polymer type, diluent type, and polymer amount had distinct effects on the swelling and erosion behavior of the ER matrix tablets. Compared with those with isomalt (G720) or dextrate (DXT), formulations with polyethylene glycol 8000 (P8000) consistently exhibited greater swelling. Additionally, higher molecular weight was correlated with increased swelling within the same polymer type. Hydroxypropylmethylcellulose (HPMC) and polyethylene oxide (PEO)-based formulations showed higher swelling rates, while polyvinyl alcohol (PVA-80) displayed the highest erosion percentage. The findings highlight the significance of incorporating early-stage screening designs to maximize efficiency and optimize time and resource. This approach enables the development of a comprehensive understanding of drug release mechanisms from ER matrix tablets.

cyclicpeptide: a Python package for cyclic peptide drug design

The unique cyclic structure of cyclic peptides grants them remarkable stability and bioactivity, making them powerful candidates for treating various diseases. However, the lack of standardized tools for cyclic peptide data has hindered their potential in today's artificial intelligence-driven efficient drug design landscape. To bridge this gap, here we introduce a Python package named cyclicpeptide specifically for cyclic peptide drug design. This package provides standardized tools such as Structure2Sequence, Sequence2Structure, and format transformation to process, convert, and standardize cyclic peptide structure and sequence data. Additionally, it includes GraphAlignment for cyclic peptide-specific alignment and search and PropertyAnalysis to enhance the understanding of their drug-like properties and potential applications. This comprehensive suite of tools aims to streamline the integration of cyclic peptides into modern drug discovery pipelines, accelerating the development of cyclic peptide-based therapeutics.

Solubilization techniques used for poorly water-soluble drugs

About 40% of approved drugs and nearly 90% of drug candidates are poorly water-soluble drugs. Low solubility reduces the drugability. Effectively improving the solubility and bioavailability of poorly water-soluble drugs is a critical issue that needs to be urgently addressed in drug development and application. This review briefly introduces the conventional solubilization techniques such as solubilizers, hydrotropes, cosolvents, prodrugs, salt modification, micronization, cyclodextrin inclusion, solid dispersions, and details the crystallization strategies, ionic liquids, and polymer-based, lipid-based, and inorganic-based carriers in improving solubility and bioavailability. Some of the most commonly used approved carrier materials for solubilization techniques are presented. Several approved poorly water-soluble drugs using solubilization techniques are summarized. Furthermore, this review summarizes the solubilization mechanism of each solubilization technique, reviews the latest research advances and challenges, and evaluates the potential for clinical translation. This review could guide the selection of a solubilization approach, dosage form, and administration route for poorly water-soluble drugs. Moreover, we discuss several promising solubilization techniques attracting increasing attention worldwide.

The Dawn of a New Pharmaceutical Epoch: Can AI and Robotics Reshape Drug Formulation?

Over the last four decades, pharmaceutical companies' expenditures on research and development have increased 51-fold. During this same time, clinical success rates for new drugs have remained unchanged at about 10 percent, predominantly due to lack of efficacy and/or safety concerns. This persistent problem underscores the need to innovate across the entire drug development process, particularly in drug formulation, which is often deprioritized and under-resourced.

A new artificial intelligence system for both stomach and small-bowel capsule endoscopy

BACKGROUND AND AIMS

Despite the benefits of artificial intelligence in small-bowel (SB) capsule endoscopy (CE) image reading, information on its application in the stomach and SB CE is lacking.

METHODS

In this multicenter, retrospective diagnostic study, gastric imaging data were added to the deep learning-based SmartScan (SS), which has been described previously. A total of 1069 magnetically controlled GI CE examinations (comprising 2,672,542 gastric images) were used in the training phase for recognizing gastric pathologies, producing a new artificial intelligence algorithm named SS Plus. A total of 342 fully automated, magnetically controlled CE examinations were included in the validation phase. The performance of both senior and junior endoscopists with both the SS Plus-assisted reading (SSP-AR) and conventional reading (CR) modes was assessed.

RESULTS

SS Plus was designed to recognize 5 types of gastric lesions and 17 types of SB lesions. SS Plus reduced the number of CE images required for review to 873.90 (median, 1000; interquartile range [IQR], 814.50-1000) versus 44,322.73 (median, 42,393; IQR, 31,722.75-54,971.25) for CR. Furthermore, with SSP-AR, endoscopists took 9.54 minutes (median, 8.51; IQR, 6.05-13.13) to complete the CE video reading. In the 342 CE videos, SS Plus identified 411 gastric and 422 SB lesions, whereas 400 gastric and 368 intestinal lesions were detected with CR. Moreover, junior endoscopists remarkably improved their CE image reading ability with SSP-AR.

CONCLUSIONS

Our study shows that the newly upgraded deep learning-based algorithm SS Plus can detect GI lesions and help improve the diagnostic performance of junior endoscopists in interpreting CE videos.

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available. Scientific contribution Our research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

Effect of Co-exposure to Additional Substances on the Bioconcentration of Per(poly)fluoroalkyl Substances: A Meta-Analysis Based on Hydroponic Experimental Evidence

A consensus has yet to emerge regarding the bioconcentration responses of per(poly)fluoroalkyl substances under co-exposure with other additional substances in aqueous environments. This study employed a meta-analysis to systematically investigate the aforementioned issues on the basis of 1,085 published datasets of indoor hydroponic simulation experiments. A hierarchical meta-analysis model with an embedded variance covariance matrix was constructed to eliminate the non-independence and shared controls of the data. Overall, the co-exposure resulted in a notable reduction in PFAS bioaccumulation (cumulative effect size, CES =  - 0.4287, p < 0.05) and bioconcentration factor (R2 = 0.9507, k < 1, b < 0) in hydroponics. In particular, the inhibition of PFAS bioconcentration induced by dissolved organic matter (percentage form of the effect size, ESP =  - 48.98%) was more pronounced than that induced by metal ions (ESP =  - 35.54%), particulate matter (ESP =  - 24.70%) and persistent organic pollutants (ESP =  - 18.66%). A lower AS concentration and a lower concentration ratio of ASs to PFASs significantly promote PFAS bioaccumulation (p < 0.05). The bioaccumulation of PFASs with long chains or high fluoride contents tended to be exacerbated in the presence of ASs. Furthermore, the effect on PFAS bioaccumulation was also significantly dependent on the duration of co-exposure (p < 0.05). The findings of this study provide novel insights into the fate and bioconcentration of PFAS in aquatic environments under co-exposure conditions.

Predicting tissue distribution and tumor delivery of nanoparticles in mice using machine learning models

Nanoparticles (NPs) can be designed for targeted delivery in cancer nanomedicine, but the challenge is a low delivery efficiency (DE) to the tumor site. Understanding the impact of NPs' physicochemical properties on target tissue distribution and tumor DE can help improve the design of nanomedicines. Multiple machine learning and artificial intelligence models, including linear regression, support vector machine, random forest, gradient boosting, and deep neural networks (DNN), were trained and validated to predict tissue distribution and tumor delivery based on NPs' physicochemical properties and tumor therapeutic strategies with the dataset from Nano-Tumor Database. Compared to other machine learning models, the DNN model had superior predictions of DE to tumors and major tissues. The determination coefficients (R2) for the test datasets were 0.41, 0.42, 0.45, 0.79, 0.87, and 0.83 for DE in tumor, heart, liver, spleen, lung, and kidney, respectively. All the R2 and root mean squared error (RMSE) results of the test datasets were similar to the 5-fold cross validation results. Feature importance analysis showed that the core material of NPs played an important role in output predictions among all physicochemical properties. Furthermore, multiple NP formulations with greater DE to the tumor were determined by the DNN model. To facilitate model applications, the final model was converted to a web dashboard. This model could serve as a high-throughput pre-screening tool to support the design of new and efficient nanomedicines with greater tumor DE and serve as an alternative tool to reduce, refine, and partially replace animal experimentation in cancer nanomedicine research.

Exploring the Artificial Intelligence and Its Impact in Pharmaceutical Sciences: Insights Toward the Horizons Where Technology Meets Tradition

The technological revolutions in computers and the advancement of high-throughput screening technologies have driven the application of artificial intelligence (AI) for faster discovery of drug molecules with more efficiency, and cost-friendly finding of hit or lead molecules. The ability of software and network frameworks to interpret molecular structures' representations and establish relationships/correlations has enabled various research teams to develop numerous AI platforms for identifying new lead molecules or discovering new targets for already established drug molecules. The prediction of biological activity, ADME properties, and toxicity parameters in early stages have reduced the chances of failure and associated costs in later clinical stages, which was observed at a high rate in the tedious, expensive, and laborious drug discovery process. This review focuses on the different AI and machine learning (ML) techniques with their applications mainly focused on the pharmaceutical industry. The applications of AI frameworks in the identification of molecular target, hit identification/hit-to-lead optimization, analyzing drug-receptor interactions, drug repurposing, polypharmacology, synthetic accessibility, clinical trial design, and pharmaceutical developments are discussed in detail. We have also compiled the details of various startups in AI in this field. This review will provide a comprehensive analysis and outline various state-of-the-art AI/ML techniques to the readers with their framework applications. This review also highlights the challenges in this field, which need to be addressed for further success in pharmaceutical applications.

Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

The Artificial Intelligence-Powered New Era in Pharmaceutical Research and Development: A Review

Currently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) are gaining increased interest in many fields, particularly in pharmaceutical research and development, where they assist in decision-making in complex situations. Numerous research studies and advancements have demonstrated how these computational technologies are used in various pharmaceutical research and development aspects, including drug discovery, personalized medicine, drug formulation, optimization, predictions, drug interactions, pharmacokinetics/ pharmacodynamics, quality control/quality assurance, and manufacturing processes. Using advanced modeling techniques, these computational technologies can enhance efficiency and accuracy, handle complex data, and facilitate novel discoveries within minutes. Furthermore, these technologies offer several advantages over conventional statistics. They allow for pattern recognition from complex datasets, and the models, typically developed from data-driven algorithms, can predict a given outcome (model output) from a set of features (model inputs). Additionally, this review discusses emerging trends and provides perspectives on the application of AI with quality by design (QbD) and the future role of AI in this field. Ethical and regulatory considerations associated with integrating AI into pharmaceutical technology were also examined. This review aims to offer insights to researchers, professionals, and others on the current state of AI applications in pharmaceutical research and development and their potential role in the future of research and the era of pharmaceutical Industry 4.0 and 5.0.

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks

Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important.

CardioDPi: An explainable deep-learning model for identifying cardiotoxic chemicals targeting hERG, Cav1.2, and Nav1.5 channels

The cardiotoxic effects of various pollutants have been a growing concern in environmental and material science. These effects encompass arrhythmias, myocardial injury, cardiac insufficiency, and pericardial inflammation. Compounds such as organic solvents and air pollutants disrupt the potassium, sodium, and calcium ion channels cardiac cell membranes, leading to the dysregulation of cardiac function. However, current cardiotoxicity models have disadvantages of incomplete data, ion channels, interpretability issues, and inability of toxic structure visualization. Herein, an interpretable deep-learning model known as CardioDPi was developed, which is capable of discriminating cardiotoxicity induced by the human Ether-à-go-go-related gene (hERG) channel, sodium channel (Na_v1.5), and calcium channel (Ca_v1.5) blockade. External validation yielded promising area under the ROC curve (AUC) values of 0.89, 0.89, and 0.94 for the hERG, Na_v1.5, and Ca_v1.5 channels, respectively. The CardioDPi can be freely accessed on the web server CardioDPipredictor (http://cardiodpi.sapredictor.cn/). Furthermore, the structural characteristics of cardiotoxic compounds were analyzed and structural alerts (SAs) can be extracted using the user-friendly CardioDPi-SAdetector web service (http://cardiosa.sapredictor.cn/). CardioDPi is a valuable tool for identifying cardiotoxic chemicals that are environmental and health risks. Moreover, the SA system provides essential insights for mode-of-action studies concerning cardiotoxic compounds.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Optimization and evaluation of modified release solid dosage forms using artificial neural network

This study aims to optimize and evaluate drug release kinetics of Modified-Release (MR) solid dosage form of Quetiapine Fumarate MR tablets by using the Artificial Neural Networks (ANNs). In training the neural network, the drug contents of Quetiapine Fumarate MR tablet such as Sodium Citrate, Eudragit® L100 55, Eudragit® L30 D55, Lactose Monohydrate, Dicalcium Phosphate (DCP), and Glyceryl Behenate were used as variable input data and Drug Substance Quetiapine Fumarate, Triethyl Citrate, and Magnesium Stearate were used as constant input data for the formulation of the tablet. The in-vitro dissolution profiles of Quetiapine Fumarate MR tablets at ten different time points were used as a target data. Several layers together build the neural network by connecting the input data with the output data via weights, these weights show importance of input nodes. The training process optimises the weights of the drug product excipients to achieve the desired drug release through the simulation process in MATLAB software. The percentage drug release of predicted formulation matched with the manufactured formulation using the similarity factor (f2), which evaluates network efficiency. The ANNs have enormous potential for rapidly optimizing pharmaceutical formulations with desirable performance characteristics.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Peptide-Hitchhiking for the Development of Nanosystems in Glioblastoma

Glioblastoma (GBM) remains the epitome of aggressiveness and lethality in the spectrum of brain tumors, primarily due to the blood-brain barrier (BBB) that hinders effective treatment delivery, tumor heterogeneity, and the presence of treatment-resistant stem cells that contribute to tumor recurrence. Nanoparticles (NPs) have been used to overcome these obstacles by attaching targeting ligands to enhance therapeutic efficacy. Among these ligands, peptides stand out due to their ease of synthesis and high selectivity. This article aims to review single and multiligand strategies critically. In addition, it highlights other strategies that integrate the effects of external stimuli, biomimetic approaches, and chemical approaches as nanocatalytic medicine, revealing their significant potential in treating GBM with peptide-functionalized NPs. Alternative routes of parenteral administration, specifically nose-to-brain delivery and local treatment within the resected tumor cavity, are also discussed. Finally, an overview of the significant obstacles and potential strategies to overcome them are discussed to provide a perspective on this promising field of GBM therapy.

Predictive computational models for assessing the impact of co-milling on drug dissolution

Co-milling is an effective technique for improving dissolution rate limited absorption characteristics of poorly water-soluble drugs. However, there is a scarcity of models available to forecast the magnitude of dissolution rate improvement caused by co-milling. Therefore, this study endeavoured to quantitatively predict the increase in dissolution by co-milling based on drug properties. Using a biorelevant dissolution setup, a series of 29 structurally diverse and crystalline drugs were screened in co-milled and physically blended mixtures with Polyvinylpyrrolidone K25. Co-Milling Dissolution Ratios after 15 min (COMDR15 min) and 60 min (COMDR60 min) drug release were predicted by variable selection in the framework of a partial least squares (PLS) regression. The model forecasts the COMDR15 min (R2 = 0.82 and Q2 = 0.77) and COMDR60 min (R2 = 0.87 and Q2 = 0.84) with small differences in root mean square errors of training and test sets by selecting four drug properties. Based on three of these selected variables, applicable multiple linear regression equations were developed with a high predictive power of R2 = 0.83 (COMDR15 min) and R2 = 0.84 (COMDR60 min). The most influential predictor variable was the median drug particle size before milling, followed by the calculated drug logD6.5 value, the calculated molecular descriptor Kappa 3 and the apparent solubility of drugs after 24 h dissolution. The study demonstrates the feasibility of forecasting the dissolution rate improvements of poorly water-solube drugs through co-milling. These models can be applied as computational tools to guide formulation in early stage development.

Intravitreal therapeutic nanoparticles for age-related macular degeneration: Design principles, progress and opportunities

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly. The current standard treatment for AMD involves frequent intravitreal administrations of therapeutic agents. While effective, this approach presents challenges, including patient discomfort, inconvenience, and the risk of adverse complications. Nanoparticle-based intravitreal drug delivery platforms offer a promising solution to overcome these limitations. These platforms are engineered to target the retina specifically and control drug release, which enhances drug retention, improves drug concentration and bioavailability at the retinal site, and reduces the frequency of injections. This review aims to uncover the design principles guiding the development of highly effective nanoparticle-based intravitreal drug delivery platforms for AMD treatment. By gaining a deeper understanding of the physiology of ocular barriers and the physicochemical properties of nanoparticles, we establish a basis for designing intravitreal nanoparticles to optimize drug delivery and drug retention in the retina. Furthermore, we review recent nanoparticle-based intravitreal therapeutic strategies to highlight their potential in improving AMD treatment efficiency. Lastly, we address the challenges and opportunities in this field, providing insights into the future of nanoparticle-based drug delivery to improve therapeutic outcomes for AMD patients.

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 normally 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., 1215 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 prevented degradation of drug. Moreover, the 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®.

Mechanisms and Barriers in Nanomedicine: Progress in the Field and Future Directions

In recent years, steady progress has been made in synthesizing and characterizing engineered nanoparticles, resulting in several approved drugs and multiple promising candidates in clinical trials. Regulatory agencies such as the Food and Drug Administration and the European Medicines Agency released important guidance documents facilitating nanoparticle-based drug product development, particularly in the context of liposomes and lipid-based carriers. Even with the progress achieved, it is clear that many barriers must still be overcome to accelerate translation into the clinic. At the recent conference workshop "Mechanisms and Barriers in Nanomedicine" in May 2023 in Colorado, U.S.A., leading experts discussed the formulation, physiological, immunological, regulatory, clinical, and educational barriers. This position paper invites open, unrestricted, nonproprietary discussion among senior faculty, young investigators, and students to trigger ideas and concepts to move the field forward.

In silico prediction of ocular toxicity of compounds using explainable machine learning and deep learning approaches

The accurate identification of chemicals with ocular toxicity is of paramount importance in health hazard assessment. In contemporary chemical toxicology, there is a growing emphasis on refining, reducing, and replacing animal testing in safety evaluations. Therefore, the development of robust computational tools is crucial for regulatory applications. The performance of predictive models is heavily reliant on the quality and quantity of data. In this investigation, we amalgamated the most extensive dataset (4901 compounds) sourced from governmental GHS-compliant databases and literature to develop binary classification models of chemical ocular toxicity. We employed 12 molecular representations in conjunction with six machine learning algorithms and two deep learning algorithms to create a series of binary classification models. The findings indicated that the deep learning method GCN outperformed the machine learning models in cross-validation, achieving an impressive AUC of 0.915. However, the top-performing machine learning model (RF-Descriptor) demonstrated excellent performance with an AUC of 0.869 on the test set and was therefore selected as the best model. To enhance model interpretability, we conducted the SHAP method and attention weights analysis. The two approaches offered visual depictions of the relevance of key descriptors and substructures in predicting ocular toxicity of chemicals. Thus, we successfully struck a delicate balance between data quality and model interpretability, rendering our model valuable for predicting and comprehending potential ocular-toxic compounds in the early stages of drug discovery.

Deep Learning-Driven Exploration of Pyrroloquinoline Quinone Neuroprotective Activity in Alzheimer's Disease

Alzheimer's disease (AD) is a pressing concern in neurodegenerative research. To address the challenges in AD drug development, especially those targeting Aβ, this study uses deep learning and a pharmacological approach to elucidate the potential of pyrroloquinoline quinone (PQQ) as a neuroprotective agent for AD. Using deep learning for a comprehensive molecular dataset, blood-brain barrier (BBB) permeability is predicted and the anti-inflammatory and antioxidative properties of compounds are evaluated. PQQ, identified in the Mediterranean-DASH intervention for a diet that delays neurodegeneration, shows notable BBB permeability and low toxicity. In vivo tests conducted on an Aβ₁₋₄₂-induced AD mouse model verify the effectiveness of PQQ in reducing cognitive deficits. PQQ modulates genes vital for synapse and anti-neuronal death, reduces reactive oxygen species production, and influences the SIRT1 and CREB pathways, suggesting key molecular mechanisms underlying its neuroprotective effects. This study can serve as a basis for future studies on integrating deep learning with pharmacological research and drug discovery.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Design of a Reciprocal Injection Device for Stability Studies of Parenteral Biological Drug Products

Formulation screening, essential for assessing the impact of physical, chemical, and mechanical stresses on protein stability, plays a critical role in biologics drug product development. This research introduces a Reciprocal Injection Device (RID) designed to accelerate formulation screening by probing protein stability under intensified stress conditions within prefilled syringes. This versatile device is designed to accommodate a broad spectrum of injection parameters and diverse syringe dimensions. A commercial drug product was employed as a model monoclonal antibody formulation. Our findings effectively highlight the efficacy of the RID in assessing concentration-dependent protein stability. This device exhibits significant potential to amplify the influences of interfacial interactions, such as those with buffer salts, excipients, air, metals, and silicone oils, commonly found in combination drug products, and to evaluate the protein stability under varied stresses.

Activating SRC/MAPK signaling via 5-HT1A receptor contributes to the effect of vilazodone on improving thrombocytopenia

Thrombocytopenia caused by long-term radiotherapy and chemotherapy exists in cancer treatment. Previous research demonstrates that 5-Hydroxtrayptamine (5-HT) and its receptors induce the formation of megakaryocytes (MKs) and platelets. However, the relationships between 5-HT1A receptor (5-HTR1A) and MKs is unclear so far. We screened and investigated the mechanism of vilazodone as a 5-HTR1A partial agonist in promoting MK differentiation and evaluated its therapeutic effect in thrombocytopenia. We employed a drug screening model based on machine learning (ML) to screen the megakaryocytopoiesis activity of Vilazodone (VLZ). The effects of VLZ on megakaryocytopoiesis were verified in HEL and Meg-01 cells. Tg (itga2b: eGFP) zebrafish was performed to analyze the alterations in thrombopoiesis. Moreover, we established a thrombocytopenia mice model to investigate how VLZ administration accelerates platelet recovery and function. We carried out network pharmacology, Western blot, and immunofluorescence to demonstrate the potential targets and pathway of VLZ. VLZ has been predicted to have a potential biological action. Meanwhile, VLZ administration promotes MK differentiation and thrombopoiesis in cells and zebrafish models. Progressive experiments showed that VLZ has a potential therapeutic effect on radiation-induced thrombocytopenia in vivo. The network pharmacology and associated mechanism study indicated that SRC and MAPK signaling are both involved in the processes of megakaryopoiesis facilitated by VLZ. Furthermore, the expression of 5-HTR1A during megakaryocyte differentiation is closely related to the activation of SRC and MAPK. Our findings demonstrated that the expression of 5-HTR1A on MK, VLZ could bind to the 5-HTR1A receptor and further regulate the SRC/MAPK signaling pathway to facilitate megakaryocyte differentiation and platelet production, which provides new insights into the alternative therapeutic options for thrombocytopenia.

Piquing artificial intelligence towards drug discovery: Tools, techniques, and applications

The purpose of this study was to discuss how artificial intelligence (AI) methods have affected the field of drug development. It looks at how AI models and data resources are reshaping the drug development process by offering more affordable and expedient options to conventional approaches. The paper opens with an overview of well-known information sources for drug development. The discussion then moves on to molecular representation techniques that make it possible to convert data into representations that computers can understand. The paper also gives a general overview of the algorithms used in the creation of drug discovery models based on AI. In particular, the paper looks at how AI algorithms might be used to forecast drug toxicity, drug bioactivity, and drug physicochemical properties. De novo drug design, binding affinity prediction, and other AI-based models for drug-target interaction were covered in deeper detail. Modern applications of AI in nanomedicine design and pharmacological synergism/antagonism prediction were also covered. The potential advantages of AI in drug development are highlighted as the evaluation comes to a close. It underlines how AI may greatly speed up and improve the efficiency of drug discovery, resulting in the creation of new and better medicines. To fully realize the promise of AI in drug discovery, the review acknowledges the difficulties that come with its uses in this field and advocates for more study and development.

Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.

Exploiting Nanotechnology for Drug Delivery: Advancing the Anti-Cancer Effects of Autophagy-Modulating Compounds in Traditional Chinese Medicine

Background

Cancer continues to be a prominent issue in the field of medicine, as demonstrated by recent studies emphasizing the significant role of autophagy in the development of cancer. Traditional Chinese Medicine (TCM) provides a variety of anti-tumor agents capable of regulating autophagy. However, the clinical application of autophagy-modulating compounds derived from TCM is impeded by their restricted water solubility and bioavailability. To overcome this challenge, the utilization of nanotechnology has been suggested as a potential solution. Nonetheless, the current body of literature on nanoparticles delivering TCM-derived autophagy-modulating anti-tumor compounds for cancer treatment is limited, lacking comprehensive summaries and detailed descriptions.

Methods

Up to November 2023, a comprehensive research study was conducted to gather relevant data using a variety of databases, including PubMed, ScienceDirect, Springer Link, Web of Science, and CNKI. The keywords utilized in this investigation included "autophagy", "nanoparticles", "traditional Chinese medicine" and "anticancer".

Results

This review provides a comprehensive analysis of the potential of nanotechnology in overcoming delivery challenges and enhancing the anti-cancer properties of autophagy-modulating compounds in TCM. The evaluation is based on a synthesis of different classes of autophagy-modulating compounds in TCM, their mechanisms of action in cancer treatment, and their potential benefits as reported in various scholarly sources. The findings indicate that nanotechnology shows potential in enhancing the availability of autophagy-modulating agents in TCM, thereby opening up a plethora of potential therapeutic avenues.

Conclusion

Nanotechnology has the potential to enhance the anti-tumor efficacy of autophagy-modulating compounds in traditional TCM, through regulation of autophagy.

De Novo Generation and Identification of Novel Compounds with Drug Efficacy Based on Machine Learning

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable activity. Traditional drug development typically begins with target selection, but the correlation between targets and disease remains to be further investigated, and drugs designed based on targets may not always have the desired drug efficacy. The emergence of machine learning provides a powerful tool to overcome the challenge. Herein, a machine learning-based strategy is developed for de novo generation of novel compounds with drug efficacy termed DTLS (Deep Transfer Learning-based Strategy) by using dataset of disease-direct-related activity as input. DTLS is applied in two kinds of disease: colorectal cancer (CRC) and Alzheimer's disease (AD). In each case, novel compound is discovered and identified in in vitro and in vivo disease models. Their mechanism of actionis further explored. The experimental results reveal that DTLS can not only realize the generation and identification of novel compounds with drug efficacy but also has the advantage of identifying compounds by focusing on protein targets to facilitate the mechanism study. This work highlights the significant impact of machine learning on the design of novel compounds with drug efficacy, which provides a powerful new approach to drug discovery.

3D printing of biologics-what has been accomplished to date?

Three-dimensional (3D) printing is a promising approach for the stabilization and delivery of non-living biologics. This versatile tool builds complex structures and customized resolutions, and has significant potential in various industries, especially pharmaceutics and biopharmaceutics. Biologics have become increasingly prevalent in the field of medicine due to their diverse applications and benefits. Stability is the main attribute that must be achieved during the development of biologic formulations. 3D printing could help to stabilize biologics by entrapment, support binding, or crosslinking. Furthermore, gene fragments could be transited into cells during co-printing, when the pores on the membrane are enlarged. This review provides: (i) an introduction to 3D printing technologies and biologics, covering genetic elements, therapeutic proteins, antibodies, and bacteriophages; (ii) an overview of the applications of 3D printing of biologics, including regenerative medicine, gene therapy, and personalized treatments; (iii) information on how 3D printing could help to stabilize and deliver biologics; and (iv) discussion on regulations, challenges, and future directions, including microneedle vaccines, novel 3D printing technologies and artificial-intelligence-facilitated research and product development. Overall, the 3D printing of biologics holds great promise for enhancing human health by providing extended longevity and enhanced quality of life, making it an exciting area in the rapidly evolving field of biomedicine.

Predicting Calcein Release from Ultrasound-Targeted Liposomes: A Comparative Analysis of Random Forest and Support Vector Machine

OBJECTIVE

This study presents a comparative analysis of RF and SVM for predicting calcein release from ultrasound-triggered, targeted liposomes under varied low-frequency ultrasound (LFUS) power densities (6.2, 9, and 10 mW/cm2).

METHODS

Liposomes loaded with calcein and targeted with seven different moieties (cRGD, estrone, folate, Herceptin, hyaluronic acid, lactobionic acid, and transferrin) were synthesized using the thin-film hydration method. The liposomes were characterized using Dynamic Light Scattering and Bicinchoninic Acid assays. Extensive data collection and preprocessing were performed. RF and SVM models were trained and evaluated using mean absolute error (MAE), mean squared error (MSE), coefficient of determination (R²), and the a20 index as performance metrics.

RESULTS

RF consistently outperformed SVM, achieving R2 scores above 0.96 across all power densities, particularly excelling at higher power densities and indicating a strong correlation with the actual data.

CONCLUSION

RF outperforms SVM in drug release prediction, though both show strengths and apply based on specific prediction needs.