PharmDE: A new expert system for drug-excipient compatibility evaluation

Citric acid cross-linked pomegranate peel extract-loaded pH-responsive β-cyclodextrin/carboxymethyl tapioca starch hydrogel film for diabetic wound healing

Pomegranate peel extract (PPE) hydrogel films filled with citric acid (CA) and β-cyclodextrin-carboxymethyl tapioca starch (CMS) were designed mainly to prevent wound infections and speed up the healing process. FTIR and NMR studies corroborated the carboxymethylation of neat tapioca starch (NS). CMS exhibited superior swelling behavior than NS. The amount of CA and β-CD controlled the physicochemical parameters of developed PPE/CA/β-CD/CMS films. Optimized film (OF) exhibited acceptable swellability, wound fluid absorptivity, water vapor transmission rate, water contact angle, and mechanical properties. Biodegradable, biocompatible, and antibacterial films exhibited pH dependence in the release of ellagic acid for up to 24 h. In mice model, PPE/CA/β-CD/CMS hydrogel film treatment showed promising wound healing effects, including increased collagen deposition, reduced inflammation, activation of the Wingless-related integration site (wnt) pathway leading to cell division, proliferation, and migration to the wound site. The expression of the WNT3A gene did not show any significant differences among all the studied groups. Developed PPE-loaded CA/β-CD/CMS film promoted wound healing by epithelialization, granulation tissue thickness, collagen deposition, and angiogenesis, hence could be recommended as a biodegradable and antibacterial hydrogel platform to improve the cell proliferation during the healing of diabetic wounds.

Towards safer and efficient formulations: Machine learning approaches to predict drug-excipient compatibility

Predicting drug-excipient compatibility is a critical aspect of pharmaceutical formulation design. In this study, we introduced an innovative approach that leverages machine learning techniques to improve the accuracy of drug-excipient compatibility predictions. Mol2vec and 2D molecular descriptors combined with the stacking technique were used to improve the performance of the model. This approach achieved a significant advancement in the predictive capacity as demonstrated by the accuracy, precision, recall, AUC, and MCC of 0.98, 0.87, 0.88, 0.93 and 0.86, respectively. Using the DE-INTERACT model as the benchmark, our stacking model could remarkably detect drug-excipient incompatibility in 10/12 tested cases, while DE-INTERACT managed to recognize only 3 out of 12 incompatibility cases in the validation experiments. To ensure user accessibility, the trained model was deployed to a user-friendly web platform (URL: https://decompatibility.streamlit.app/). This interactive interface accommodated inputs through various types, including names, PubChem CID, or SMILES strings. It promptly generated compatibility predictions alongside corresponding probability scores. However, the continual refinement of model performance is crucial before applying this model in practice.

Predicting Autoxidation of Sulfides in Drug-like Molecules Using Quantum Mechanical/Density Functional Theory Methods

Autoxidation of drugs and drug-like molecules is a major concern in the development of safe and effective therapeutics. Because active pharmaceutical ingredients (APIs) that contain sulfur atoms can form sulfoxides under oxidative stress, predicting oxidative susceptibilities within an organic molecule can have a major impact in accelerating the compound's stability assessment. For investigation of a sulfur atom's oxidative stability, density functional theory (DFT) methods were applied to accurately predict S-O estimated bond dissociation enthalpies (BDEs) of sulfoxides. Our process employed B3LYP/6-31+G(d) for geometry optimization and frequency calculation, and we employed B3P86/6-311++G(2df,2p) to obtain electronic energies from single-point energy calculations. A total of 84 drug-like molecules containing 50 different sulfide scaffolds were used to develop a risk scale. Our results showed that when S-O BDE is less than 69 kcal/mol, the sulfur atom has low oxidative susceptibility. High oxidation risk occurs when the S-O BDE is greater than 75 kcal/mol. The risk scale was successful in predicting the relative propensities of sulfide oxidation among the small organic molecules and commercial drugs examined.

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.

Interdisciplinary advances reshape the delivery tools for effective NASH treatment

BACKGROUND

Nonalcoholic steatohepatitis (NASH), a severe systemic and inflammatory subtype of nonalcoholic fatty liver disease, eventually develops into cirrhosis and hepatocellular carcinoma with few options for effective treatment. Currently potent small molecules identified in preclinical studies are confronted with adverse effects and long-term ineffectiveness in clinical trials. Nevertheless, highly specific delivery tools designed from interdisciplinary concepts may address the significant challenges by either effectively increasing the concentrations of drugs in target cell types, or selectively manipulating the gene expression in liver to resolve NASH.

SCOPE OF REVIEW

We focus on dissecting the detailed principles of the latest interdisciplinary advances and concepts that direct the design of future delivery tools to enhance the efficacy. Recent advances have indicated that cell and organelle-specific vehicles, non-coding RNA research (e.g. saRNA, hybrid miRNA) improve the specificity, while small extracellular vesicles and coacervates increase the cellular uptake of therapeutics. Moreover, strategies based on interdisciplinary advances drastically elevate drug loading capacity and delivery efficiency and ameliorate NASH and other liver diseases.

MAJOR CONCLUSIONS

The latest concepts and advances in chemistry, biochemistry and machine learning technology provide the framework and strategies for the design of more effective tools to treat NASH, other pivotal liver diseases and metabolic disorders.

Preparation of PEGylated nedaplatin liposomes with sustained release behavior for enhancing the antitumor efficacy of non-small cell lung cancer

Nedaplatin (NDP) plays an important role in the chemotherapies of non-small cell lung cancer (NSCLC). However, dose-limiting toxicities such as myelosuppression and drug resistance restrict its clinical application. Herein, we intended to overcome these defects by developing a PEGylated liposomal formulation encapsulated NDP (NDP-LPs). For the first time, we found the incompatibility between NDP and natural phospholipids such as egg phosphatidylcholine (EPC) using the high-performance liquid chromatography (HPLC) method. The orthogonal experimental design was applied to optimize the conditions for preparing NDP-LPs, with encapsulation efficiency (EE) as the evaluation indicator. The physicochemical properties of optimized NDP-LPs were further characterized, including particle size, zeta potential, EE, drug release profiles, and so on. Results showed that a significantly sustained-release profile of NDP-LPs was observed and the releasing time of NDP could reach as long as 8 days. At the cellular level, NDP encapsulated in the PEGylated liposomes enhanced its cellular uptake and possessed potent cytotoxic activity. After intravenous injection, NDP-LPs could accumulate at tumor sites and effectivelyinhibit tumor growth of mice without obvious adverse effects. In conclusion, our results demonstrated that PEGylated liposomes could serve as a promising carrier to enhance the therapeutic effects of NDP.

The applications of machine learning to predict the forming of chemically stable amorphous solid dispersions prepared by hot-melt extrusion

Amorphous solid dispersion (ASD) is one of the most important strategies to improve the solubility and dissolution rate of poorly water-soluble drugs. As a widely used technique to prepare ASDs, hot-melt extrusion (HME) provides various benefits, including a solvent-free process, continuous manufacturing, and efficient mixing compared to solvent-based methods, such as spray drying. Energy input, consisting of thermal and specific mechanical energy, should be carefully controlled during the HME process to prevent chemical degradation and residual crystallinity. However, a conventional ASD development process uses a trial-and-error approach, which is laborious and time-consuming. In this study, we have successfully built multiple machine learning (ML) models to predict the amorphization of crystalline drug formulations and the chemical stability of subsequent ASDs prepared by the HME process. We utilized 760 formulations containing 49 active pharmaceutical ingredients (APIs) and multiple types of excipients. By evaluating the built ML models, we found that ECFP-LightGBM was the best model to predict amorphization with an accuracy of 92.8%. Furthermore, ECFP-XGBoost was the best in estimating chemical stability with an accuracy of 96.0%. In addition, the feature importance analyses based on SHapley Additive exPlanations (SHAP) and information gain (IG) revealed that several processing parameters and material attributes (i.e., drug loading, polymer ratio, drug's Extended-connectivity fingerprints (ECFP) fingerprints, and polymer's properties) are critical for achieving accurate predictions for the selected models. Moreover, important API's substructures related to amorphization and chemical stability were determined, and the results are largely consistent with the literature. In conclusion, we established the ML models to predict formation of chemically stable ASDs and identify the critical attributes during HME processing. Importantly, the developed ML methodology has the potential to facilitate the product development of ASDs manufactured by HME with a much reduced human workload.

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

Artificial Intelligence (AI)-based formulation development is a promising approach for facilitating the drug product development process. AI is a versatile tool that contains multiple algorithms that can be applied in various circumstances. Solid dosage forms, represented by tablets, capsules, powder, granules, etc., are among the most widely used administration methods. During the product development process, multiple factors including critical material attributes (CMAs) and processing parameters can affect product properties, such as dissolution rates, physical and chemical stabilities, particle size distribution, and the aerosol performance of the dry powder. However, the conventional trial-and-error approach for product development is inefficient, laborious, and time-consuming. AI has been recently recognized as an emerging and cutting-edge tool for pharmaceutical formulation development which has gained much attention. This review provides the following insights: (1) a general introduction of AI in the pharmaceutical sciences and principal guidance from the regulatory agencies, (2) approaches to generating a database for solid dosage formulations, (3) insight on data preparation and processing, (4) a brief introduction to and comparisons of AI algorithms, and (5) information on applications and case studies of AI as applied to solid dosage forms. In addition, the powerful technique known as deep learning-based image analytics will be discussed along with its pharmaceutical applications. By applying emerging AI technology, scientists and researchers can better understand and predict the properties of drug formulations to facilitate more efficient drug product development processes.

Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.

Computational pharmaceutics - A new paradigm of drug delivery

In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still relies on traditional trial-and-error experiments, which are time-consuming, costly, and unpredictable. With the exponential growth of computing capability and algorithms, in recent ten years, a new discipline named "computational pharmaceutics" integrates with big data, artificial intelligence, and multi-scale modeling techniques into pharmaceutics, which offered great potential to shift the paradigm of drug delivery. Computational pharmaceutics can provide multi-scale lenses to pharmaceutical scientists, revealing physical, chemical, mathematical, and data-driven details ranging across pre-formulation studies, formulation screening, in vivo prediction in the human body, and precision medicine in the clinic. The present paper provides a comprehensive and detailed review in all areas of computational pharmaceutics and "Pharma 4.0", including artificial intelligence and machine learning algorithms, molecular modeling, mathematical modeling, process simulation, and physiologically based pharmacokinetic (PBPK) modeling. We not only summarized the theories and progress of these technologies but also discussed the regulatory requirements, current challenges, and future perspectives in the area, such as talent training and a culture change in the future pharmaceutical industry.