Tablets are the most typical dosage forms of pharmaceutical inventions. Sustained-release (SR) tablet formulations are designed to release the drug gradually in the bloodstream and often require less frequent dosing. Current strategies to optimize sustained-release tablet dissolution time still rely on the traditional approach, which is time-consuming and expensive. In the present context, we have demonstrated alternate machine learning and deep learning models through the TPOT AutoML platform. Six machine learning (ML) models were compared to improve the methodology for dissolution time prediction, particularly the decision tree regressor (DTR), gradient boost regressor (GBR), random forest regressor (RFR), extra tree regressor (ETR), XGBoost regressor (XGBR), and deep learning (DL). The obtained results indicated that machine learning methods are convincing in speculating the dissolution time, especially the random forest regressor, but upon hypertuning of the deep neural network, the deep learning model with a 10-fold cross-validation scheme demonstrated superior predictive performance with an NRMSE of 8% and an R² of 0.92. The major essentials affecting the dissolution time of SR tablets were explained using the SHAP method.

dataset development, deep learning, machine learning model, SHAP method, TPOT autoML

Pharmaceutical design needs to be tailored for all novel drug entities due to the variability in these relationships (Hayashi et al. 2023). The primary goal of many pharmaceutical drugs is to attain a consistent blood level that is both helpful for therapeutic purposes and safe over a long period of time (Mishra et al. 2019). Therefore, sustained-release (SR) tablet dosage forms are intended to achieve an extended therapeutic impact. SR dosage forms aim to enhance drug efficacy by prolonging therapeutic effects, allowing for less frequent dosing, and utilizing lower doses (Bhagat et al. 2023). They are also considered a wider choice for drugs with quick life spans, which typically need regular administration to sustain their therapeutic levels. Overall, a lower dosage and less frequent dosing can contribute to fewer complications from the drugs. This is particularly beneficial in the treatment of chronic diseases, where long-term adherence to medication is crucial (Mishra et al. 2019). There has been a notable increase in SR drug delivery systems over the last two to three decades due to various factors like the high cost of raising the latest drug candidates, the running out of current patents, and the development of newly discovered polymers that enable controlled drug release (Bhagat et al. 2023).

The choice of polymer material, including hydrophilic and hydrophobic substances or biodegradable materials, shows the versatility of formulations. Studies of in vitro dissolution can be used to determine the medication release rate (Santoshrao et al. 2014). A formulation can possess many elements, like polymers, lubricants, solubilizers, binders, and fillers. The choice of formulation components depends on the specific dosage form and the requirements of the formulation procedure. Each element and its specific quantity directly impact the overall critical quality attributes (CQA) of a dosage form (Szlek et al. 2022).

Current strategies to optimize the dissolution time of SR tablets still rely on conventional trial-and-error methods, which are considered time-consuming and expensive. There may be a need for more efficient and streamlined approaches to enhance the optimization process (Yanga et al. 2019). One way to disrupt the conventional approach is by executing machine learning (ML) and deep learning (DL) models. ML has indeed become a transformative force in various research domains, including pharmaceuticals. ML models can make data-driven predictions with datasets of preliminary data.

ML methods, such as optimization, can be employed to find optimal combinations of ingredients and process parameters that produce the required properties, such as drug release profiles. This can lead to significant cost savings in terms of materials, resources, and time. ML models can contribute to maintaining product consistency by identifying key factors that influence the quality of formulations (Sustersic et al. 2023). DL is indeed a subset of ML that has gained significant attention and success in various domains of artificial intelligence. DL includes training artificial neural networks with several layers (DNNs) to learn and make predictions or choices.

This method has proven to be quite successful in a variety of applications, including natural language processing, image and speech recognition, and many others. Therefore, DL has indeed demonstrated remarkable success in various domains, often outperforming traditional ML methods in predictive performance. In vitro, prediction of a pharmaceutical formulation (Yanga et al. 2019), prediction of adverse drug reactions in drug discovery (Mohsen et al. 2021), prediction of pharmacological drug properties (Aliper et al. 2016), and DL-based dose prediction (Gronberg et al. 2023)—as many such works relaying on DL models were published in the last 5 years. The prediction of dissolution release by artificial neural network (ANN) and regression methods, respectively (Wang et al. 2022), prediction of HPMC polymer matrix particle size distribution (PSD) by using ANN, support vector machine (SVM), and ETR (Galatta et al. 2021), surface area and volume of the tablet affect the release profile of the tablet can be predicted by ANN (Mazur et al. 2023), and physiochemical and powder characteristics of API are predicted by four-layered ANN (Takayama et al. 2017). Multivariant tools and ANN methods have been seen to execute well in the context of understating the underlying relationship between CQAs and process parameters.

Nevertheless, ML and DL modeling methods can be used to assess the dissolution time of SR tablets with the above-mentioned inferences. However, with more comparisons of DL with ML, more approaches are looking forward to anticipating the effective formulation. In the present context, we developed an optimal method for the evaluation of the dissolution time of SR tablets based on ML models, namely DTR, GBR, XGBoost regressor, RFR, ETR, and DL. All these models have demonstrated successful applications in the fields of pharmaceutical formulation development, development processes, and destructive analytical tests. Therefore, by leveraging data-driven approaches, these methods contribute to more efficient and effective pharmaceutical development and manufacturing processes. Nevertheless, it is essential to remember that the successful implementation of these techniques requires careful consideration of data quality, model interpretability, and regulatory compliance in the pharmaceutical industry (Loua et al. 2021). This paper showcases a wide range of ML and DL methods for accurately forecasting the dissolution time of SR tablets.

Solid oral dosage forms occupy a major part in the field of pharmaceuticals, and their efficacy is highly dependent on the absorption of API in the human body. The major aspect that impacts the API is its dissolution behavior, and that is essential for ensuring the bioavailability and therapeutic effectiveness of the API. However, the dissolution profile is mostly influenced by the physical characteristics of the materials, like solubility and filling material employed in the development procedure, and the process variables, like compression force. Therefore, it often involves a careful balance of formulation design, choice of materials, and control of manufacturing processes.

Regulatory agencies often set specifications for dissolution profiles to ensure the consistency and efficacy of the product. The traditional method for dissolution involves testing a small number of tablets, which may be laborious, prolonged, and overpriced. Additionally, it would not provide a complete representation of the overall dissolution behavior. As a result, there is interest in developing surrogate or alternate methods that can provide insights into dissolution attributes without the need for extensive testing (Fink et al. 2023). On account of this, an alternative method was employed: ML models are perhaps powerful function approximators and can memorize and forecast composite systems from huge amounts of input.

Lately, ML has been productively implemented in pharmaceutical research in the formulation development area. The developed models are successful with greater accuracy in predicting the dissolution curves of SR tablets (Hana et al. 2019). DL is one of the most extensively utilized ML methods. DL and ML have revolutionized the world’s perspective. Optimizing the formulation of an SR dosage form is a crucial step in pharmaceutical development. To achieve this efficiently, researchers often employ statistical experimental designs, which are valuable techniques for systematically exploring the relationships between formulation variables and the dissolution rate. These help in achieving the desired dissolution times with minimal experimentation.

In the present study, as mentioned in the methodology segment, the developed models have been analyzed by ML techniques to forecast which produces the greatest results. Regarding the outcomes displayed in Table 1, the outputs are as per the 5-fold cross-validation scheme. The ML technique, especially RFR, has shown the best in TPOT AUTOML analysis, but upon hypertuning, the DL model results in greater R² and NRMSE%. After the TPOT AUTOML output analysis, we went ahead with the final model development of a 10-fold cross-validation scheme where the DL model was trained over 2200 epochs, fetching us better results than previous ones. Moreover, in Fig. 2, critical parameters that affect the dissolution time were plotted. A clear analysis was conducted to figure out the fundamentals underlying the final model’s forecasting by employing Shapley values. The findings not only provide insights into dissolution time prediction but also reinforce the suitability of autoML-based approaches for addressing the challenges posed by complex pharmaceutical tasks. However, the results showed that DL has achieved significant outcomes compared to the other ML models. Therefore, our predictions were consistent (Hana et al. 2018; Yanga et al. 2019).

Figure 2. 

Scatter plot between actual and predictive values for dissolution time of the DL model.

Figure 3. 

SHAP dependence diagram for the ML models for the top 20 attributes.

SR tablets represent a notable advancement in formulation development, especially when compared to the traditional “trial-and-error” methods that have been in use for hundreds of years. The traditional approach often involves repeated experimentation and refinement, consuming significant time, financial resources, and human efforts. The present contemporary research has progressed the ML and DL models to exhibit a beneficial estimation for the dissolution time of SR tablet formulations. In summary, the employed models could constructively anticipate the key attributes, but the results showed that predictions with DL were better than those of other ML models. The proposed DL model not only streamlines SR tablet development but also showcases potential applications across different dosage forms and pharmaceutical research areas. Its contribution to reducing development timelines, optimizing resources, and facilitating robust drug product development underscores its significance in advancing pharmaceutical formulation research.

The authors express their gratitude to the SRM College of Pharmacy for their insightful recommendations.