State-of-the-Art Review of Artificial Neural Networks to Predict, Characterize and Optimize Pharmaceutical Formulation

Enhancing quality-by-design through weighted goal programming: a case study on formulation of ultradeformable liposomes

INTRODUCTION

Optimization of pharmaceutical formulations requires advanced tools to ensure quality, safety, and efficacy. quality-by-design (QbD), introduced by the FDA, emphasizes understanding and controlling processes early in development. Advanced optimization methods, such as desirability, have surpassed traditional single-objective techniques. Others, such as weighted goal programming (WGP) offers unique advantages by integrating decision-maker preferences, enabling balanced solutions for complex drug delivery systems. This study applies WGP to optimize timolol (TM)-loaded nanoliposomes aligning with QbD principles.

METHODS

The optimization process followed six steps: identifying factors and responses, developing a Design of Experiments (DoE) plan, defining ideal and anti-ideal points, setting aspiration levels, assigning relative weights, and applying WGP compared to desirability function. Minimized and balanced deviations from aspiration levels served as criteria for selecting the most robust optimization results. Six responses were analyzed: vesicle size ( z 1 ) , polydispersity index ( z 2 ) , zeta potential ( z 3 ) , deformability index ( z 4 ) , phosphorus content ( z 5 ) , and drug entrapment efficiency ( z 6 ) .

RESULTS

WGP produced a more balanced formulation that simultaneously optimized multiple responses. By incorporating the importance of each response, the WGP approach improved control over size, colloidal stability, and drug entrapment, based on its mathematical formulation. Comparative analysis with the desirability function confirmed that WGP effectively addressed potential tradeoffs without oversimplifying conflicting objectives.

CONCLUSIONS

This case-study demonstrates WGP potential as an advanced multi-objective optimization tool for pharmaceutical applications, improving upon traditional methods in complex formulations. Its ability to harmonize multiple critical attributes in line with QbD highlights its value in developing high-quality pharmaceutical products.

Utilizing machine learning for predicting drug release from polymeric drug delivery systems

Polymeric drug delivery systems (PDDS) play a crucial role in controlled drug release, providing improved therapeutic outcomes. However, formulating PDDS and predicting their release profiles remain challenging due to their complex structures and the numerous variables that influence their behavior. Traditional mathematical and empirical prediction methods are limited in capturing these complexities. Recent studies have unveiled the potential of Machine Learning (ML) in revolutionizing drug delivery, particularly in formulating complex PDDS. This article provides an overview of the significant and fundamental principles of various ML strategies in estimating PDDS drug release behavior. Our focus extends to the accomplishments and pivotal discoveries in current research, spanning seven distinct sustained-release drug delivery systems: matrix tablets, microspheres, implants, hydrogels, films, 3D-printed dosage forms, and other innovations. Furthermore, it addresses the challenges associated with ML-based drug release prediction and presents current solutions while delving into future perspectives. Our investigation underscores the significance of Artificial Neural Networks in ML-based PDDS release profile prediction, surpassing both traditional and alternative ML-based methods. These extensive datasets can be drawn from literature-based resources or enhanced through specific algorithms. Moreover, ensemble-based models have proven advantageous in scenarios involving intricate relationships, such as a high number of output parameters. ML-based drug release prediction notably exhibits substantial promise in 3D-printed dosage forms, presenting a frontier for personalized medicine and precise drug delivery.

BRAFPred: A Novel Approach for Accurate Prediction of the B-Type Rapidly Accelerated Fibrosarcoma Inhibitor

B-type rapidly accelerated fibrosarcoma (BRAF) is a key oncogene that regulates cell signaling and proliferation, rendering it a crucial target for cancer therapeutics. Traditional QSAR methods are hindered by their reliance on a singular model, their inability to grasp complex nonlinearities, and limited generalization, undermining predictive efficacy. To address these challenges, we introduce BRAFPred, a novel framework that leverages stacked ensemble learning to integrate both classical machine learning and advanced deep learning techniques for the precise prediction of BRAF inhibitors. We utilized 12 handcrafted molecular descriptors derived from PaDeL, in conjunction with small molecule sequence features, as foundational inputs. Furthermore, we employed extreme gradient boosting (XGB), support vector regression (SVR), and deep learning architectures based on Chemprop and a pretrained BERT model (FG-BERT) to generate additional predictive features. These multisource features were subsequently integrated within a meta-ensemble random forest regression model, which utilized 26 input variables. Empirical results demonstrate that BRAFPred significantly outperforms benchmark models, achieving a mean absolute error (MAE) of 0.383 and a coefficient of determination (R 2) of 0.855, surpassing Chemprop (MAE = 0.443, R 2 = 0.803), FG-BERT (MAE = 0.460, R 2 = 0.785), and Stack_BRAF (MAE = 0.403, R 2 = 0.839). Extensive evaluation on benchmark data sets affirms BRAFPred's superiority over state-of-the-art methodologies, with robust generalization capabilities demonstrated on blind test sets. Additionally, ablation studies and case analyses underscore the robustness of the model's design. The source code, data sets, and prediction results for BRAFPred are available for further research at https://github.com/EvanZhang1216/BRAFPred.

Machine learning-assisted design and prediction of materials for batteries based on alkali metals

Since the commercialization of lithium-ion batteries in the 1990s, batteries based on alkali metals have been promising candidates for energy storage. The performances of these batteries, in terms of cost-efficiency, energy density, safety, and cycle life need continuous improvement. Battery performances are highly dependent on electrode materials, yet the long experimental period, intensive labor, and high cost remain bottlenecks in the improvement of electrode materials. Machine learning (ML), which is being increasingly integrated into materials science, offers transformative potential by reducing the R&D period and cost. ML also demonstrates significant advantages in the performance prediction of various materials, and it can also help reveal the structure-performance relationship of materials. ML-assisted material design and performance prediction thus enable the innovation of advanced materials. Herein, implementation of ML for exploring alkali metal-based batteries is outlined, highlighting various ML algorithms as well as electrode reaction mechanisms.

Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review

BACKGROUND

To summarize the statistical performance of machine learning in predicting revision, secondary knee injury, or reoperations following anterior cruciate ligament reconstruction (ACLR), and to provide a general overview of the statistical performance of these models.

METHODS

Three online databases (PubMed, MEDLINE, EMBASE) were searched from database inception to February 6, 2024, to identify literature on the use of machine learning to predict revision, secondary knee injury (e.g. anterior cruciate ligament (ACL) or meniscus), or reoperation in ACLR. The authors adhered to the PRISMA and R-AMSTAR guidelines as well as the Cochrane Handbook for Systematic Reviews of Interventions. Demographic data and machine learning specifics were recorded. Model performance was recorded using discrimination, area under the curve (AUC), concordance, calibration, and Brier score. Factors deemed predictive for revision, secondary injury or reoperation were also extracted. The MINORS criteria were used for methodological quality assessment.

RESULTS

Nine studies comprising 125,427 patients with a mean follow-up of 5.82 (0.08-12.3) years were included in this review. Two of nine (22.2%) studies served as external validation analyses. Five (55.6%) studies reported on mean AUC (strongest model range 0.77-0.997). Four (44.4%) studies reported mean concordance (strongest model range: 0.67-0.713). Two studies reported on Brier score, calibration intercept, and calibration slope, with values ranging from 0.10 to 0.18, 0.0051-0.006, and 0.96-0.97 amongst highest performing models, respectively. Four studies reported calibration error, with all four studies demonstrating significant miscalibration at either two or five-year follow-ups amongst 10 of 14 models assessed.

CONCLUSION

Machine learning models designed to predict the risk of revision or secondary knee injury demonstrate variable discriminatory performance when evaluated with AUC or concordance metrics. Furthermore, there is variable calibration, with several models demonstrating evidence of miscalibration at two or five-year marks. The lack of external validation of existing models limits the generalizability of these findings. Future research should focus on validating current models in addition to developing new multimodal neural networks to improve accuracy and reliability.

Optimizing Neuroprotective Nano-structured Lipid Carriers for Transdermal Delivery through Artificial Neural Network

BACKGROUND

Dementia associated with Alzheimer's disease (AD) is a neurological disorder. AD is a progressive neurodegenerative condition that predominantly impacts the elderly population, although it can also manifest in younger people through the impairment of cognitive functions, such as memory, cognition, and behaviour. Donepezil HCl and Memantine HCl are encapsulated in Nanostructured Lipid Carriers (NLCs) to prolong systemic circulation and minimize the systemic side effects.

OBJECTIVE

This work explores the use of data mining tools to optimize the formulation of NLCs comprising of Donepezil HCl and Memantine HCl for transdermal drug delivery. Neuroprotective drugs and excipients are utilized for protecting the nervous system against damage or degeneration.

METHODS

The NLCs were formulated using a high-speed homogenization technique followed by ultrasonication. NLCs were optimized using Box Behnken Design (BBD) in Design Expert Software and artificial neural network (ANN) in IBM SPSS statistics. The independent variables included the ratio of solid lipid to liquid lipid, the percentage of surfactant, and the revolutions per minute (RPM) of the high-speed homogenizer.

RESULTS

The NLCs that were formulated had a mean particle size ranging from 67.0±0.45 to 142.4±0.52 nm. Both drugs have a %EE range over 75%, and Zeta potential was determined to be - 26±0.36 mV. CryoSEM was used to do the structural study. The permeation study showed the prolonged release of the formulation.

CONCLUSION

The results indicate that NLCs have the potential to be a carrier for transporting medications to deeper layers of the skin and reaching systemic circulation, making them a suitable formulation for the management of Dementia. Both ANN and BBD techniques are effective tools for systematically developing and optimizing NLC formulation.

Explainable deep recurrent neural networks for the batch analysis of a pharmaceutical tableting process in the spirit of Pharma 4.0

Due to the continuously increasing Cost of Goods Sold, the pharmaceutical industry has faced several challenges, and the Right First-Time principle with data-driven decision-making has become more pressing to sustain competitiveness. Thus, in this work, three different types of artificial neural network (ANN) models were developed, compared, and interpreted by analyzing an open-access dataset from a real pharmaceutical tableting production process. First, the multilayer perceptron (MLP) model was used to describe the total waste based on 20 raw material properties and 25 statistical descriptors of the time series data collected throughout the tableting (e.g., tableting speed and compression force). Then using 10 process time series data in addition to the raw material properties, the cumulative waste, during manufacturing was also predicted by long short-term memory (LSTM) and bidirectional LSTM (biLSTM) recurrent neural networks (RNN). The LSTM network was used to forecast the waste production profile to allow preventive actions. The results showed that RNNs were able to predict the waste trajectory, the best model resulting in 1096 and 2174 tablets training and testing root mean squared errors, respectively. For a better understanding of the process, and the models and to help the decision-support systems and control strategies, interpretation methods were implemented for all ANNs, which increased the process understanding by identifying the most influential material attributes and process parameters. The presented methodology is applicable to various critical quality attributes in several fields of pharmaceutics and therefore is a useful tool for realizing the Pharma 4.0 concept.

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.

Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration

The pharmaceutical industry has faced significant changes in recent years, primarily influenced by regulatory standards, market competition, and the need to accelerate drug development. Model-informed drug development (MIDD) leverages quantitative computational models to facilitate decision-making processes. This approach sheds light on the complex interplay between the influence of a drug's performance and the resulting clinical outcomes. This comprehensive review aims to explain the mechanisms that control the dissolution and/or release of drugs and their subsequent permeation through biological membranes. Furthermore, the importance of simulating these processes through a variety of in silico models is emphasized. Advanced compartmental absorption models provide an analytical framework to understand the kinetics of transit, dissolution, and absorption associated with orally administered drugs. In contrast, for topical and transdermal drug delivery systems, the prediction of drug permeation is predominantly based on quantitative structure-permeation relationships and molecular dynamics simulations. This review describes a variety of modeling strategies, ranging from mechanistic to empirical equations, and highlights the growing importance of state-of-the-art tools such as artificial intelligence, as well as advanced imaging and spectroscopic techniques.

What is the current state of artificial intelligence applications in dentistry and orthodontics?

BACKGROUND

The use of Artificial Intelligence (AI) in the medical field has the potential to bring about significant improvements in patient care and outcomes. AI is being used in dentistry and more specifically in orthodontics through the development of diagnostic imaging tools, the development of treatment planning tools, and the development of robotic surgery. The aim of this study is to present the latest emerging AI softwares and applications in dental field to benefit from.

TYPES OF STUDIES REVIEWED

Search strategies were conducted in three electronic databases, with no date limits in the following databases up to April 30, 2023: MEDLINE, PUBMED, and GOOGLE® SCHOLAR for articles related to AI in dentistry & orthodontics. No inclusion and exclusion criteria were used for the selection of the articles. Most of the articles included (n = 79) are reviews of the literature, retro/prospective studies, systematic reviews and meta-analyses, and observational studies.

RESULTS

The use of AI in dentistry and orthodontics is a rapidly growing area of research and development, with the potential to revolutionize the field and bring about significant improvements in patient care and outcomes; this can save clinicians' chair-time and push for more individualized treatment plans. Results from the various studies reported in this review are suggestive that the accuracy of AI-based systems is quite promising and reliable.

PRACTICAL IMPLICATIONS

AI application in the healthcare field has proven to be efficient and helpful for the dentist to be more precise in diagnosis and clinical decision-making. These systems can simplify the tasks and provide results in quick time which can save dentists time and help them perform their duties more efficiently. These systems can be of greater aid and can be used as auxiliary support for dentists with lesser experience.

Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside

Artificial intelligence (AI) techniques have the potential to revolutionize drug release modeling, optimize therapy for personalized medicine, and minimize side effects. By applying AI algorithms, researchers can predict drug release profiles, incorporate patient-specific factors, and optimize dosage regimens to achieve tailored and effective therapies. This AI-based approach has the potential to improve treatment outcomes, enhance patient satisfaction, and advance the field of pharmaceutical sciences. International collaborations and professional organizations play vital roles in establishing guidelines and best practices for data collection and sharing. Open data initiatives can enhance transparency and scientific progress, facilitating algorithm validation.

StackBRAF: A Large-Scale Stacking Ensemble Learning for BRAF Affinity Prediction

The B-rapidly accelerated fibrosarcoma (BRAF) is a proto-oncogene that plays a vital role in cell signaling and growth regulation. Identifying a potent BRAF inhibitor can enhance therapeutic success in high-stage cancers, particularly metastatic melanoma. In this study, we proposed a stacking ensemble learning framework for the accurate prediction of BRAF inhibitors. We obtained 3857 curated molecules with BRAF inhibitory activity expressed as a predicted half-maximal inhibitory concentration value (pIC₅₀) from the ChEMBL database. Twelve molecular fingerprints from PaDeL-Descriptor were calculated for model training. Three machine learning algorithms including extreme gradient boosting, support vector regression, and multilayer perceptron were utilized for constructing new predictive features (PFs). The meta-ensemble random forest regression, called StackBRAF, was created based on the 36 PFs. The StackBRAF model achieves lower mean absolute error (MAE) and higher coefficient of determination (R² and Q²) than the individual baseline models. The stacking ensemble learning model provides good y-randomization results, indicating a strong correlation between molecular features and pIC₅₀. An applicability domain of the model with an acceptable Tanimoto similarity score was also defined. Moreover, a large-scale high-throughput screening of 2123 FDA-approved drugs against the BRAF protein was successfully demonstrated using the StackBRAF algorithm. Thus, the StackBRAF model proved beneficial as a drug design algorithm for BRAF inhibitor drug discovery and drug development.

Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges

Deep learning has achieved remarkable progress, particularly in neuroimaging analysis. Deep learning applications have also been extended from adult to pediatric medical images, and thus, this paper aims to present a systematic review of this recent research. We first introduce the commonly used deep learning methods and architectures in neuroimaging, such as convolutional neural networks, auto-encoders, and generative adversarial networks. A non-exhaustive list of commonly used publicly available pediatric neuroimaging datasets and repositories are included, followed by a categorical review of recent works in pediatric MRI-based deep learning studies in the past five years. These works are categorized into recognizing neurodevelopmental disorders, identifying brain and tissue structures, estimating brain age/maturity, predicting neurodevelopment outcomes, and optimizing MRI brain imaging and analysis. Finally, we also discuss the recent achievements and challenges on these applications of deep learning to pediatric neuroimaging.

Pharma 4.0-Artificially Intelligent Digital Twins for Solidified Nanosuspensions

Digital twins capacitate the industry 4.0 paradigm by predicting and optimizing the performance of physical assets of interest, mirroring a realistic in-silico representation of their functional behaviour. Although advanced digital twins set forth disrupting opportunities by delineating the in-service product and the related process dynamic performance, they have yet to be adopted by the pharma sector. The latter, currently struggles more than ever before to improve solubility of BCS II i.e., hard-to-dissolve active pharmaceutical ingredients by micronization and subsequent stabilization. Herein we construct and functionally validate the first artificially intelligent digital twin thread, capable of describing the course of manufacturing of such solidified nanosuspensions given a defined lifecycle starting point and predict and optimize the relevant process outcomes. To this end, we referenced experimental data as the sampling source, which we then augmented via pattern recognition utilizing neural network propagations. The zeta-dynamic potential metrics of the nanosuspensions were correlated to the interfacial Gibbs energy, while the density and heat capacity of the material system was calculated via the Saft-γ-Mie statistical fluid theory. The curated data was then fused to physical and empirical laws to choose the appropriate theory and numeric description, respectively, before being polished by tuning the critical parameters to achieve the best fit with reality.