In-line particle size measurement during granule fluidization using convolutional neural network-aided process imaging

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.

Process analytical technology based quality assurance of API concentration and fiber diameter of electrospun amorphous solid dispersions

In this study, a novel quality assurance system was developed utilizing Process analytical technology (PAT) tools and artificial intelligence (AI). Our goal was to monitor the critical quality attributes (CQAs) like drug concentration, morphology and fiber diameter of electrospun amorphous solid dispersion (ASD) formulations with fast at-line techniques. Doxycycline-hyclate (DOX), a tetracycline-type antibiotic was used as a model drug with 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as the matrix excipient. The water-based formulations were electrospun with high-speed electrospinning (HSES). Raman and NIR sensors and machine vision-based color measurement techniques were employed to accurately determine the drug concentration. Given that morphology can influence the solubility of the drug, a convolutional neural network (CNN)-based AI model was developed to examine this property and detect manufacturing defects. Additionally, the diameter of electrospun fibrous samples was measured using camera images and a trained AI model, enabling rapid analysis of fiber diameter with results similar to that of scanning electron microscopy (SEM). These methods and models demonstrate potential in-line analytical tools, offering rapid, cheap and non-destructive analysis of ASD formulations.

Automated particle inspection of continuously freeze-dried products using computer vision

The pharmaceutical industry is progressing towards more continuous manufacturing techniques. To dry biopharmaceuticals, continuous freeze drying has several advantages on manufacturing and process analytical control compared to batch freeze-drying, including better visual inspection potential. Visual inspection of every freeze-dried product is a key quality assessment after the lyophilization process to ensure that freeze-dried products are free from foreign particles and defects. This quality assessment is labor-intensive for operators who need to assess thousands of samples for an extensive amount of time leading to certain drawbacks. Applying Artificial Intelligence, specifically computer vision, on high-resolution images from every freeze-dried product can quantitatively and qualitatively outperform human visual inspection. For this study, continuously freeze-dried samples were prepared based on a real-world pharmaceutical product using manually induced particles of different sizes and subsequently imaged using a tailor-made setup to develop an image dataset (with particle sizes from 50μm to 1 mm) used to train multiple object detection models. You Only Look Once version 7 (YOLOv7) outperforms human inspection by a large margin, obtaining particle detection precision of up to 88.9% while controlling the recall at 81.2%, thus detecting most of the object present in the images, with an inference time of less than 1 s per vial.

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.

Exploring the Potential of Artificial Intelligence as a Facilitating Tool for Formulation Development in Fluidized Bed Processor: a Comprehensive Review

This in-depth study looks into how artificial intelligence (AI) could be used to make formulation development easier in fluidized bed processes (FBP). FBP is complex and involves numerous variables, making optimization challenging. Various AI techniques have addressed this challenge, including machine learning, neural networks, genetic algorithms, and fuzzy logic. By integrating AI with experimental design, process modeling, and optimization strategies, intelligent systems for FBP can be developed. The advantages of AI in this context include improved process understanding, reduced time and cost, enhanced product quality, and robust formulation optimization. However, data availability, model interpretability, and regulatory compliance challenges must be addressed. Case studies demonstrate successful applications of AI in decision-making, process outcome prediction, and scale-up. AI can improve efficiency, quality, and cost-effectiveness in significant ways. Still, it is important to think carefully about data quality, how easy it is to understand, and how to follow the rules. Future research should focus on fully harnessing the potential of AI to advance formulation development in FBP.

Feature extraction of particle morphologies of pharmaceutical excipients from scanning electron microscope images using convolutional neural networks

Scanning electron microscopy (SEM) images are the most widely used tool for evaluating particle morphology; however, quantitative evaluation using SEM images is time-consuming and often neglected. In this study, we aimed to extract features related to particle morphology of pharmaceutical excipients from SEM images using a convolutional neural network (CNN). SEM images of 67 excipients were acquired and used as models. A classification CNN model of the excipients was constructed based on the SEM images. Further, features were extracted from the middle layer of this CNN model, and the data was compressed to two dimensions using uniform manifold approximation and projection. Lastly, hierarchical clustering analysis (HCA) was performed to categorize the excipients into several clusters and identify similarities among the samples. The classification CNN model showed high accuracy, allowing each excipient to be identified with a high degree of accuracy. HCA revealed that the 67 excipients were classified into seven clusters. Additionally, the particle morphologies of excipients belonging to the same cluster were found to be very similar. These results suggest that CNN models are useful tools for extracting information and identifying similarities among the particle morphologies of excipients.