Emerging Artificial Intelligence (AI) Technologies Used in the Development of Solid Dosage Forms

From conception to consumption: Applications of semi-solid extrusion 3D printing in oral drug delivery

Semi-Solid Extrusion 3D printing (SSE 3DP) has emerged as a promising technology for fabricating oral drug formulations, offering significant opportunities for personalized medicine and tailored therapeutic outcomes. SSE 3DP is particularly advantageous for producing soft and chewable drug products and is well-suited for formulations containing thermosensitive drugs due to its low-temperature printing process. Among various 3D printing techniques, SSE 3DP holds considerable potential for point-of-care applications, enabling the on-demand production of patient-specific dosage forms. Despite these advantages, SSE 3DP faces certain limitations that affect its overall development and widespread adoption. This review provides a comprehensive overview of SSE 3DP's fundamental principles, current applications, and future prospects in oral drug delivery. It also addresses the challenges and limitations associated with SSE 3DP and examines the current outlook of this technique in oral drug delivery applications. An example of such a challenge is the lack of a harmonized method for evaluating rheological properties. To address this issue, the review describes a methodology for obtaining information related to extrudability and shape fidelity from rheological properties. Overall, this review aims to highlight the transformative potential of SSE 3DP in the pharmaceutical landscape, paving the way for tailored, and patient-centric therapies.

Deep learning-based defect detection in film-coated tablets using a convolutional neural network

Film-coating is a critical step in pharmaceutical manufacturing. Traditional visual inspections for film-coated tablet defect assessment are subjective, inefficient, and labor-intensive. We propose a novel approach utilizing machine learning and image analysis to address these limitations. Here, defects of four types- chipping, breaking, color non-uniformity and speckling, were manually induced in red-orange film-coated placebo tablets. Utilizing a 3-D printed tray and a unique segmentation approach, images of good and defective tablets were collected. A convolutional neural network (CNN) was employed to quantitatively analyze the defects. The model was trained on a comprehensive dataset of 25,200 images of tablets, augmented through various transformations to improve robustness. The CNN's performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. The multi-class classification model demonstrated an accuracy of 99.7% in detection of defects in film-coated tablets, clearly outperforming static rule-based method which had 45%, 45% and 70% error in detecting dimensions- major axis, minor axis, and surface area of the tablets, respectively. This work demonstrates a valuable tool for pharmaceutical manufacturers, providing a standardized, objective, and efficient method for defect detection in tablets and presents a promising solution for ensuring product quality and accelerating the development of new pharmaceutical products.

Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery

In recent years, the pharmaceutical industry has increasingly emphasized the role of lead compound identification in developing new therapeutic agents. Lead compounds show promising pharmacological activity against specific targets and are critical in drug development. Integrative computational approaches streamline this process by efficiently screening chemical libraries and designing potential drug candidates. This chapter highlights various computational techniques for lead compound discovery, including molecular modeling, cheminformatics, ligand- and structure-based drug design, molecular dynamics simulations, ADMET prediction, drug-target interaction analysis, and high-throughput screening. These methods improve drug discovery's efficiency, cost-effectiveness, and target-specific focus. Computational pharmaceutics has gained popularity due to the longer formulation development time which in turn increases the cost as well as decrease in the drug discovery production. Conventionally, formulation development relied on costly and unpredictable trial-and-error methods. However, analyzing the big data, artificial intelligence, and multi-scale modeling in computational pharmaceutics is transforming drug delivery. This chapter provides valuable insights throughout pre-formulation, formulation screening, in vivo predictions, and personalized medicine applications. Multiscale computational modeling is advancing drug delivery systems, enabling targeted treatments with multifunctional nanoparticles. Although in its early stages, this approach helps understand complex interactions between drugs, delivery systems, and patients.

Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development

Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development. The Product Information files for all centrally authorized drug products containing a small molecule active ingredient were retrieved programmatically from the European Medicines Agency Web site. Text processing isolated relevant information, including the maximum clinical dose, dosage form, route of administration, excipients, and pharmacokinetic performance. Chemical and bioactivity data were integrated through automated linking to external curated databases. The capability of this database to inform oral medicine development was assessed in the context of drug-likeness evaluation, excipient selection, and prediction of oral fraction absorbed. Existing filters of drug-likeness, such as the Rule of Five, were found to poorly capture the chemical space of marketed oral drug products. Association rule learning identified frequent patterns in tablet formulation compositions that can be used to establish excipient combinations that have seen clinical success. Binary prediction models of oral fraction absorbed constructed exclusively from regulatory data achieved acceptable performance (balanced accuracytest = 0.725), demonstrating its modelability and potential for use during early stage molecule prioritization tasks. This study illustrates the impact of highly linked drug product data in accelerating clinical translation and underlines the ongoing need for accuracy and completeness of data reported in the regulatory datasphere.

Synchrotron computed tomography combined with AI-based image analysis for the advanced characterization of spray dried amorphous solid dispersion particles

Particle engineering aims to design particles with specific properties. A deeper understanding of how particle formation relates to material attributes and process conditions are critical to strengthen knowledge on powder properties and enhance modeling capabilities. New, alternative powder characterization techniques can offer novel and more accurate measures for particle properties, giving more advanced characterization information. In this context, a case study is presented in which spray dried amorphous solid dispersion powders produced by modifying process conditions were characterized by both well-established compendial methods (i.e., laser light diffraction, SEM image analysis, bulk and tapped density, and gas adsorption), as well as a new method combining synchrotron computed tomography (SyncCT) with AI-based image analysis. SyncCT was used to classify and quantify the spray dried particles as hollow spheres and solid particles, giving a more detailed quality measure of the particle shape, as they impact downstream processing differently. Moreover, hollow particle wall thicknesses, as well as internal and external particle surface areas were measured by SyncCT. Altogether, powder characterization data from SyncCT show similar trends to that obtained from compendial techniques and giving additional quality measure regarding particle shape, showing promise of this new and advanced characterization method.

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Understanding of Wetting Mechanism Toward the Sticky Powder and Machine Learning in Predicting Granule Size Distribution Under High Shear Wet Granulation

The granulation of traditional Chinese medicine (TCM) has attracted widespread attention, there is limited research on the high shear wet granulation (HSWG) and wetting mechanisms of sticky TCM powders, which profoundly impact the granule size distribution (GSD). Here we investigate the wetting mechanism of binders and the influence of various parameters on the GSD of HSWG and establish a GSD prediction model. Permeability and contact angle experiments combined with molecular dynamics (MD) simulations were used to explore the wetting mechanism of hydroalcoholic solutions with TCM powder. Machine learning (ML) was employed to build a GSD prediction model, feature importance explained the influence of features on the predictive performance of the model, and correlation analysis was used to assess the influence of various parameters on GSD. The results show that water increases powder viscosity, forming high-viscosity aggregates, while ethanol primarily acted as a wetting agent. The contact angle of water on the powder bed was the largest and decreased with an increase in ethanol concentration. Extreme Gradient Boosting (XGBoost) outperformed other models in overall prediction accuracy in GSD prediction, the binder had the greatest impact on the predictions and GSD, adjusting the amount and concentration of adhesive can control the adhesion and growth of granules while the impeller speed had the least influence on granulation. The study elucidates the wetting mechanism and provides a GSD prediction model, along with the impact of material properties, formulation, and process parameters obtained, aiding the intelligent manufacturing and formulation development of TMC.

In-line NIR coupled with machine learning to predict mechanical properties and dissolution profile of PLA-Aspirin

In the production of polymeric drug delivery devices, dissolution profile and mechanical properties of the drug loaded polymeric matrix are considered important Critical Quality Attributes (CQA) for quality assurance. However, currently the industry relies on offline testing methods which are destructive, slow, labour intensive, and costly. In this work, a real-time method for predicting these CQAs in a Hot Melt Extrusion (HME) process is explored using in-line NIR and temperature sensors together with Machine Learning (ML) algorithms. The mechanical and drug dissolution properties were found to vary significantly with changes in processing conditions, highlighting that real-time methods to accurately predict product properties are highly desirable for process monitoring and optimisation. Nonlinear ML methods including Random Forest (RF), K-Nearest Neighbours (KNN) and Recursive Feature Elimination with RF (RFE-RF) outperformed commonly used linear machine learning methods. For the prediction of tensile strength RFE-RF and KNN achieved R 2 values 98% and 99%, respectively. For the prediction of drug dissolution, two time points were considered with drug release at t = 6 h as a measure of the extent of burst release, and t = 96 h as a measure of sustained release. KNN and RFE-RF achieved R 2 values of 97% and 96%, respectively in predicting the drug release at t = 96 h. This work for the first time reports the prediction of drug dissolution and mechanical properties of drug loaded polymer product from in-line data collected during the HME process.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42252-024-00063-5.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

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.

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.

A Review on the Recent Advancements and Artificial Intelligence in Tablet Technology

BACKGROUND

Tablet formulation could be revolutionized by the integration of modern technology and established pharmaceutical sciences. The pharmaceutical sector can develop tablet formulations that are not only more efficient and stable but also patient-friendly by utilizing artificial intelligence (AI), machine learning (ML), and materials science.

OBJECTIVES

The primary objective of this review is to explore the advancements in tablet technology, focusing on the integration of modern technologies like artificial intelligence (AI), machine learning (ML), and materials science to enhance the efficiency, cost-effectiveness, and quality of tablet formulation processes.

METHODS

This review delves into the utilization of AI and ML techniques within pharmaceutical research and development. The review also discusses various ML methodologies employed, including artificial neural networks, an ensemble of regression trees, support vector machines, and multivariate data analysis techniques.

RESULTS

Recent studies showcased in this review demonstrate the feasibility and effectiveness of ML approaches in pharmaceutical research. The application of AI and ML in pharmaceutical research has shown promising results, offering a potential avenue for significant improvements in the product development process.

CONCLUSION

The integration of nanotechnology, AI, ML, and materials science with traditional pharmaceutical sciences presents a remarkable opportunity for enhancing tablet formulation processes. This review collectively underscores the transformative role that AI and ML can play in advancing pharmaceutical research and development, ultimately leading to more efficient, reliable and patient-centric tablet formulations.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

Application of an AI image analysis and classification approach to characterise dissolution and precipitation events in the flow through apparatus

Imaging and artificial intelligence (AI) approaches have been used with increasing frequency in pharmaceutical industry in recent years. Characterisation of processes such as drug dissolution and precipitation is vital in quality control testing and drug manufacture. To support existing techniques like in vitro dissolution testing, novel process analytical technologies (PATs) can give an insight into these processes. The aim of this study was to create and explore the potential of an automated image classification model based on image analysis to identify events (dissolution and precipitation) occurring in the flow-through apparatus (FTA) test cell, and the ability to characterise a dissolution process over time. Several precipitation conditions were tested in a USP 4 FTA test cell with images recorded during early (plume formation) and late (particulate re-formation) stages of precipitation. An available MATLAB code was used as a base to develop and validate an anomaly classification model able to detect different events occurring during the precipitation process in the dissolution cell. Two variants of the model were tested on images from a dissolution test in the FTA, with a view to application of the image analysis system to quantitative characterization of the dissolution process over time. It was found that the classification model is highly accurate (>90%) in detecting events occurring in the FTA test cell. The model showed potential to be used to characterise the stages of dissolution and precipitation processes, and as a proof of concept demonstrates potential for deep machine learning image analysis to be applied to kinetics of other pharmaceutical processes.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies

Low aqueous solubility is a common and serious challenge for most drug substances not only in development but also in the market, and it may cause low absorption and bioavailability as a result. Amorphization is an intermolecular modification strategy to address the issue by breaking the crystal lattice and enhancing the energy state. However, due to the physicochemical properties of the amorphous state, drugs are thermodynamically unstable and tend to recrystallize over time. Glass-forming ability (GFA) is an experimental method to evaluate the forming and stability of glass formed by crystallization tendency. Machine learning (ML) is an emerging technique widely applied in pharmaceutical sciences. In this study, we successfully developed multiple ML models (i.e., random forest (RF), XGBoost, and support vector machine (SVM)) to predict GFA from 171 drug molecules. Two different molecular representation methods (i.e., 2D descriptor and Extended-connectivity fingerprints (ECFP)) were implemented to process the drug molecules. Among all ML algorithms, 2D-RF performed best with the highest accuracy, AUC, and F1 of 0.857, 0.850, and 0.828, respectively, in the testing set. In addition, we conducted a feature importance analysis, and the results mostly agreed with the literature, which demonstrated the interpretability of the model. Most importantly, our study showed great potential for developing amorphous drugs by in silico screening of stable glass formers.

Top Selling (2026) Small Molecule Orphan Drugs: A Journey into Their Chemistry

This review describes, from a chemical point of view, the top "blockbuster" small molecule orphan drugs according to their forecasted sales in 2026. Orphan drugs are intended for the treatment, prevention, or diagnosis of a rare disease or condition. These molecules are mostly addressed to the treatment of rare forms of cancer. The respiratory and central nervous systems represent other common therapeutic subcategories. This work will show how the orphan drugs market has significantly grown and will account for a consistent part of prescriptions by 2026.

Predicting the Temperature Evolution during Nanomilling of Drug Suspensions via a Semi-Theoretical Lumped-Parameter Model

Although temperature can significantly affect the stability and degradation of drug nanosuspensions, temperature evolution during the production of drug nanoparticles via wet stirred media milling, also known as nanomilling, has not been studied extensively. This study aims to establish both descriptive and predictive capabilities of a semi-theoretical lumped parameter model (LPM) for temperature evolution. In the experiments, the mill was operated at various stirrer speeds, bead loadings, and bead sizes, while the temperature evolution at the mill outlet was recorded. The LPM was formulated and fitted to the experimental temperature profiles in the training runs, and its parameters, i.e., the apparent heat generation rate Q_gen and the apparent overall heat transfer coefficient times surface area UA, were estimated. For the test runs, these parameters were predicted as a function of the process parameters via a power law (PL) model and machine learning (ML) model. The LPM augmented with the PL and ML models was used to predict the temperature evolution in the test runs. The LPM predictions were also compared with those of an enthalpy balance model (EBM) developed recently. The LPM had a fitting capability with a root-mean-squared error (RMSE) lower than 0.9 °C, and a prediction capability, when augmented with the PL and ML models, with an RMSE lower than 4.1 and 2.1 °C, respectively. Overall, the LPM augmented with the PL model had both good descriptive and predictive capability, whereas the one with the ML model had a comparable predictive capability. Despite being simple, with two parameters and obviating the need for sophisticated numerical techniques for its solution, the semi-theoretical LPM generally predicts the temperature evolution similarly or slightly better than the EBM. Hence, this study has provided a validated, simple model for pharmaceutical engineers to simulate the temperature evolution during the nanomilling process, which will help to set proper process controls for thermally labile drugs.

Using Artificial Intelligence for Drug Discovery: A Bibliometric Study and Future Research Agenda

Drug discovery is usually a rule-based process that is carefully carried out by pharmacists. However, a new trend is emerging in research and practice where artificial intelligence is being used for drug discovery to increase efficiency or to develop new drugs for previously untreatable diseases. Nevertheless, so far, no study takes a holistic view of AI-based drug discovery research. Given the importance and potential of AI for drug discovery, this lack of research is surprising. This study aimed to close this research gap by conducting a bibliometric analysis to identify all relevant studies and to analyze interrelationships among algorithms, institutions, countries, and funding sponsors. For this purpose, a sample of 3884 articles was examined bibliometrically, including studies from 1991 to 2022. We utilized various qualitative and quantitative methods, such as performance analysis, science mapping, and thematic analysis. Based on these findings, we furthermore developed a research agenda that aims to serve as a foundation for future researchers.