Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm

Hot-melt extruded in situ gelling systems (MeltDrops Technology): Formulation development, in silico modelling and in vivo studies

In situ gelling systems (ISGS) can prolong retention time and bioavailability of ophthalmic solutions. The complexity and cost of ISGS avert their industrial scale-up and clinical implementation. In this study, we demonstrate novel application of hot-melt extrusion (HME) technology for continuous manufacturing of ISGS (MeltDrops Technology). Timolol maleate (TIM) and dorzolamide hydrochloride (DRZ) loaded MeltDrops were successfully developed using HME for glaucoma management, thereby resolving issues with batch manufacturing of ISGS, prolonging retention time thus improving bioavailability. The MeltDrops technology involves one-step, i.e., passing all the ingredients through an extruder at a screw speed between 20-50 rpm and barrel temperature of 80 °C. The comparative evaluation of MeltDrops and batch-processed ISGS demonstrated that MeltDrops exhibited better physical and chemical content uniformity. The extrusion temperature and screw speed were critical factors influencing content uniformity and properties of the MeltDrops. MeltDrops showed sustained drug release for >12 hours in vitro (TIM= 83.07%; DRZ = 60.43%, 12hours) versus marketed eyedrops. The developed MeltDrops followed Peppas-Sahlin model, combining Fickian diffusion and swelling processes. The in vivo study in New Zealand rabbits revealed superior effectiveness and safety of the MeltDrops as compared to the marketed eyedrops. Herein we conclude, MeltDrops would serve as a cutting-edge platform technology that can be used to manufacture various ISGS with one-step processability, cost-effectiveness, and improved product quality, which are otherwise processed by batch manufacturing that involves numerous complex processing steps.

NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing

PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established 'soft sensing' method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing.

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

During the development of a pharmaceutical formulation, a powerful tool is needed to extract the key points from the complicated process parameters and material attributes. Artificial neural networks (ANNs), a promising and more flexible modeling technique, can address real intricate questions in a high parallelism and distributed pattern in the manner of biological neural networks. The data mined and analyzing based on ANNs have the ability to replace hundreds of trial and error experiments. ANNs have been used for data analysis by pharmaceutics researchers since the 1990s and it has now become a research method in pharmaceutical science. This review focuses on the latest application progress of ANNs in the prediction, characterization and optimization of pharmaceutical formulation to provide a reference for the further interdisciplinary study of pharmaceutics and ANNs.

Machine Learning for Process Monitoring and Control of Hot-Melt Extrusion: Current State of the Art and Future Directions

In the last few decades, hot-melt extrusion (HME) has emerged as a rapidly growing technology in the pharmaceutical industry, due to its various advantages over other fabrication routes for drug delivery systems. After the introduction of the ‘quality by design’ (QbD) approach by the Food and Drug Administration (FDA), many research studies have focused on implementing process analytical technology (PAT), including near-infrared (NIR), Raman, and UV–Vis, coupled with various machine learning algorithms, to monitor and control the HME process in real time. This review gives a comprehensive overview of the application of machine learning algorithms for HME processes, with a focus on pharmaceutical HME applications. The main current challenges in the application of machine learning algorithms for pharmaceutical processes are discussed, with potential future directions for the industry.

Disrupting 3D printing of medicines with machine learning

3D printing (3DP) is a progressive technology capable of transforming pharmaceutical development. However, despite its promising advantages, its transition into clinical settings remains slow. To make the vital leap to mainstream clinical practice and improve patient care, 3DP must harness modern technologies. Machine learning (ML), an influential branch of artificial intelligence, may be a key partner for 3DP. Together, 3DP and ML can utilise intelligence based on human learning to accelerate drug product development, ensure stringent quality control (QC), and inspire innovative dosage-form design. With ML's capabilities, streamlined 3DP drug delivery could mark the next era of personalised medicine. This review details how ML can be applied to elevate the 3DP of pharmaceuticals and importantly, how it can expedite 3DP's integration into mainstream healthcare.

Artificial Intelligence in Pharmaceutical Field - A Critical Review

Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mimic the human thought process. As drug development involves high R & D costs and uncertainty in time consumption, artificial intelligence can serve as one of the promising solutions to overcome all these demerits. Due to the availability of enormous data, there are chances of missing out on some crucial details. For solving these issues, algorithms like machine learning, deep learning, and other expert systems are being used. On successful implementation of AI in the pharmaceutical field, the delays in drug development, and failure at the clinical and marketing level can be reduced. This review comprises information regarding the development of AI, its subfields, its overall implementation, and its application in the pharmaceutical sector and provides insights on challenges and limitations concerning AI.

Applications of Machine Learning in Solid Oral Dosage Form Development

This review comprehensively summarizes the application of machine learning in solid oral dosage form development over the past three decades. In both academia and industry, machine learning is increasingly applied for multiple preformulation/formulation and process development studies. Further, this review provides the authors' perspectives on how pharmaceutical scientists can use machine learning for right projects and in right ways; some key ingredients include (1) the determination of inputs, outputs, and objectives; (2) the generation of a database containing high-quality data; (3) the development of machine learning models based on dataset training and model optimization; (4) the application of trained models in making predictions for new samples. It is expected by the authors and others that machine learning will promisingly play a more important role in tomorrow's projects for solid oral dosage form development.

Coupling hot melt extrusion and fused deposition modeling: Critical properties for successful performance

Interest in 3D printing for pharmaceutical applications has increased in recent years. Compared to other 3D printing techniques, hot melt extrusion (HME)-based fused deposition modeling (FDM) 3D printing has been the most extensively investigated for patient-focused dosage. HME technology can be coupled with FDM 3D printing as a continuous manufacturing process. However, the crucial pharmaceutical polymers, formulation and process parameters must be investigated to establish HME-coupled FDM 3D printing. These advancements will lead the way towards developing continuous drug delivery systems for personalized therapy. This brief overview classifies pharmaceutical additive manufacturing, Hot Melt Extrusion, and Fused Deposition Modeling 3D printing techniques with a focus on coupling HME and FDM 3D printing processes. It also provides insights on the critical material properties, process and equipment parameters and limitations of successful HME-coupled FDM systems.