Integrated computer-aided formulation design: A case study of andrographolide/ cyclodextrin ternary formulation

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.

Computational and experimental therapeutic efficacy analysis of andrographolide phospholipid complex self-assembled nanoparticles against Neuro2a cells

BACKGROUND

Neuroblastoma is one of the most common malignancies in childhood, accounts for approximately 7% of all malignancies. Andrographolide (AN) inhibits cancer cells progression via multiple pathways like cell cycle arrest, mitochondrial apoptosis, NF-κβ inhibition, and antiangiogenesis mechanism. Despite multiple advantages, application of AN is very limited due to its low aqueous solubility (6.39 ± 0.47 μg/mL), high lipophilicity (log P ∼ 2.632 ± 0.135), and reduced stability owing to pH sensitive lactone ring.

OBJECTIVES AND RESULTS

In present investigation, a molecular complex of AN with soya-L-α-phosphatidyl choline (SPC) was synthesized as ANSPC and characterized by FT-IR and¹H NMR spectroscopy. Spectral and molecular simulation techniques confirmed the intermolecular interactions between the 14-OH group of AN and the N⁺(CH₃)₃part of SPC. In addition, molecular dynamics (MD) simulation was used to determine the degree of interaction between various proteins such as TNF-α, caspase-3, and Bcl-2. Later, ANSPC complex was transformed in to self-assembled soft nanoparticles of size 201.8 ± 1.48 nm with PDI of 0.092 ± 0.004 and zeta potential of -21.7 ± 0.85 mV. The IC50 offree AN (8.319 μg/mL) and the self-assembled soft ANSPC nanoparticles (3.406 μg/mL ∼ 1.2 μg of AN) against Neuro2a cells was estimated with significant (P < 0.05) difference. Interestingly, the self-assembled soft ANSPC nanoparticles showed better endocytosis compared to free AN in Neuro2a cells. In-vitrobiological assays confirmed that self-assembled soft ANSPC nanoparticles induces apoptosis in Neuro2a cells by declining the MMP (Δψm) and increasing the ROS generation.

CONCLUSION

Self-assembled soft ANSPC nanoparticles warrant further in-depth antitumor study in xenograft model of neuroblastoma to establish the anticancer potential.

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.

Opportunities and challenges of physiologically based pharmacokinetic modeling in drug delivery

Physiologically based pharmacokinetic (PBPK) modeling is an important in silico tool to bridge drug properties and in vivo PK behaviors during drug development. Over the recent decade, the PBPK method has been largely applied to drug delivery systems (DDS), including oral, inhaled, transdermal, ophthalmic, and complex injectable products. The related therapeutic agents have included small-molecule drugs, therapeutic proteins, nucleic acids, and even cells. Simulation results have provided important insights into PK behaviors of new dosage forms, which strongly support drug regulation. In this review, we comprehensively summarize recent progress in PBPK applications in drug delivery, which shows large opportunities for facilitating drug development. In addition, we discuss the challenges of applying this methodology from a practical viewpoint.

Development of Arteannuin B Sustained-Release Microspheres for Anti-Tumor Therapy by Integrated Experimental and Molecular Modeling Approaches

Arteannuin B (AB) has been found to demonstrate obvious anti-tumor activity. However, AB is not available for clinical use due to its very low solubility and very short half-life. This study aimed to develop AB long sustained-release microspheres (ABMs) to improve the feasibility of clinical applications. Firstly, AB-polylactic-co-glycolic acid (PLGA) microspheres were prepared by a single emulsification method. In vitro characterization studies showed that ABMs had a low burst release and stable in vitro release for up to one week. The particle size of microspheres was 69.10 μm (D50). The drug loading is 37.8%, and the encapsulation rate is 85%. Moreover, molecular dynamics modeling was firstly used to simulate the preparation process of microspheres, which clearly indicated the molecular image of microspheres and provided in-depth insights for understanding several key preparation parameters. Next, in vivo pharmacokinetics (PK) study was carried out to evaluate its sustained release effect in Sprague-Dawley (SD) rats. Subsequently, the methyl thiazolyl tetrazolium (MTT) method with human lung cancer cells (A549) was used to evaluate the in vitro efficacy of ABMs, which showed the IC50 of ABMs (3.82 μM) to be lower than that of AB (16.03 μM) at day four. Finally, in vivo anti-tumor activity and basic toxicity studies were performed on BALB/c nude mice by subcutaneous injection once a week, four times in total. The relative tumor proliferation rate T/C of AMBs was lower than 40% and lasted for 21 days after administration. The organ index, organ staining, and tumor cell staining indicated the excellent safety of ABMs than Cis-platinum. In summary, the ABMs were successfully developed and evaluated with a low burst release and a stable release within a week. Molecular dynamics modeling was firstly applied to investigate the molecular mechanism of the microsphere preparation. Moreover, the ABMs possess excellent in vitro and in vivo anti-tumor activity and low toxicity, showing great potential for clinical applications.