Machine Learning Modeling and Predictive Control of the Batch Crystallization Process

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.

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.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Development of a Digital Twin for the Prediction and Control of Supersaturation during Batch Cooling Crystallization

Fine chemicals produced via batch crystallization with properties dependent on the crystal size distribution require precise control of supersaturation, which drives the evolution of crystal size over time. Model predictive control (MPC) of supersaturation using a mechanistic model to represent the behavior of a crystallization process requires less experimental time and resources compared with fully empirical model-based control methods. Experimental characterization of the hexamine-ethanol crystallization system was performed in order to collect the parameters required to build a one-dimensional (1D) population balance model (PBM) in gPROMS FormulatedProducts software (Siemens-PSE Ltd.). Analysis of the metastable zone width (MSZW) and a series of seeded batch cooling crystallizations informed the suitable process conditions selected for supersaturation control experiments. The gPROMS model was integrated with the control software PharmaMV (Perceptive Engineering Ltd.) to create a digital twin of the crystallizer. Simulated batch crystallizations were used to train two statistical MPC blocks, allowing for in silico supersaturation control simulations to develop an effective control strategy. In the supersaturation set-point range of 0.012-0.036, the digital twin displayed excellent performance that would require minimal controller tuning to steady out any instabilities. The MPC strategy was implemented on a physical 500 mL crystallizer, with the simulated solution concentration replaced by in situ measurements from calibrated attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Physical supersaturation control performance was slightly more unstable than the in silico tests, which is consistent with expected disturbances to the heat transfer, which were not specifically modeled in simulations. Overall, the level of supersaturation control in a real crystallizer was found to be accurate and precise enough to consider future adaptations to the MPC strategy for more advanced control objectives, such as the crystal size.

Application of CO2 Supercritical Fluid to Optimize the Solubility of Oxaprozin: Development of Novel Machine Learning Predictive Models

Over the last years, extensive motivation has emerged towards the application of supercritical carbon dioxide (SCCO₂) for particle engineering. SCCO₂ has great potential for application as a green and eco-friendly technique to reach small crystalline particles with narrow particle size distribution. In this paper, an artificial intelligence (AI) method has been used as an efficient and versatile tool to predict and consequently optimize the solubility of oxaprozin in SCCO₂ systems. Three learning methods, including multi-layer perceptron (MLP), Kriging or Gaussian process regression (GPR), and k-nearest neighbors (KNN) are selected to make models on the tiny dataset. The dataset includes 32 data points with two input parameters (temperature and pressure) and one output (solubility). The optimized models were tested with standard metrics. MLP, GPR, and KNN have error rates of 2.079 × 10⁻⁸, 2.173 × 10⁻⁹, and 1.372 × 10⁻⁸, respectively, using MSE metrics. Additionally, in terms of R-squared, they have scores of 0.868, 0.997, and 0.999, respectively. The optimal inputs are the same as the maximum possible values and are paired with a solubility of 1.26 × 10⁻³ as an output.