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

Rational cyclodextrin formulation design through insights into drug release mechanism in the gastrointestinal tract via molecular dynamics simulations

Cyclodextrin formulations are crucial for enhancing the solubility of drugs. Bile salts are recognized as potential agents for displacing drugs from cyclodextrin formulations in intestinal fluids. However, the mechanism underlying this displacement remains unclear. This study aims to investigate the mechanism of competitive displacement using molecular dynamics simulations and to develop guidelines for effective cyclodextrin formulation design. The umbrella sampling method is employed to investigate the binding free energy between bile salts and cyclodextrin molecules, while metadynamics is utilized to simulate the dynamic replacement process. The results indicate that the optimal binding free energy interval between cyclodextrins and drugs ranges from -30 kJ/mol to -8 kJ/mol. Additionally, the optimal concentration ratio between drugs and cyclodextrins can be calculated based on the binding free energy. Displacement simulations showed that free single bile salt molecules are more likely to complete the displacement compared to clusters of bile salts. This suggests that the bioavailability of cyclodextrins may be higher in fasting conditions than in the fed state. This study will not only enhance our understanding of the relationships between cyclodextrin formulations and bile salts but also facilitate the rational design of more effective pharmaceutical formulations.

Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation

BCS class II candidates pose challenges in drug development due to their low solubility and permeability. Researchers have explored various techniques; co-amorphous and solid dispersion are major approaches to enhance in-vitro drug solubility and dissolution. However, in-vivo oral bioavailability remains challenging. Physiologically based pharmacokinetic (PBPK) modeling with a detailed understanding of drug absorption, distribution, metabolism, and excretion (ADME) using a mechanistic approach is emerging. This review summarizes the fundamentals of the PBPK, dissolution-absorption models, parameterization of oral absorption for BCS class II drugs, and provides information about newly emerging artificial intelligence/machine learning (AI/ML) linked PBPK approaches with their advantages, disadvantages, challenges and areas of further exploration. Additionally, the fully integrated workflow for formulation design for investigational new drugs (INDs) and virtual bioequivalence for generic molecules falling under BCS-II are discussed.

Combining High-Throughput Screening and Machine Learning to Predict the Formation of Both Binary and Ternary Amorphous Solid Dispersion Formulations for Early Drug Discovery and Development

OBJECTIVE

Amorphous solid dispersion (ASD) is widely utilized to enhance the solubility and bioavailability of water-insoluble drugs. However, conventional experimental approaches for ASD development are often resource-intensive and time-consuming. Machine learning (ML) algorithms have great potential to predict ASD formulations but face the challenge of extensive data to construct reliable models. Current study aims to predict the formation of both binary and ternary ASD by combined high-throughput screening (HTS) and ML approaches.

METHODS

Micro-quantity HTS was conducted to generate 1272 binary and ternary solid dispersions using solvent evaporation method. The Powder X-Ray Diffraction (PXRD) was used to characterize the amorphous state of formulations. The results indicated that 188 formulations successfully formed amorphous solid dispersions (ASDs), while 1084 resulted in crystalline formations. Models development employed nested cross-validation with four algorithms: Light Gradient Boosting Machine (LGBM), Random Forest (RF), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP).

RESULTS

The RF model for ASD formation achieved 96.7% accuracy on the in-house HTS dataset, with a precision of approximately 87.9% and an F1 score of 83.6%. Furthermore, the RF model trained with milligram-scale HTS experimental data could effectively predict the large-scale ASD formulations from the literature, highlighting its promise as a powerful tool for advancing ASD prediction.

CONCLUSION

In summary, the combination of HTS experiments and ML techniques provides a valuable reference framework for ASD development, greatly minimizing both time and material usage in the selection of formulations during the early stages of drug discovery with a limited quantity of API.

Insight into the Precipitation Inhibition of Polymers within Cocrystal Formulations in Solution Using Experimental and Molecular Modeling Techniques

This study investigated the role of various polymers as precipitation inhibitors in solutions of flufenamic acid (FFA) and its cocrystals with theophylline (FFA-TP) and nicotinamide (FFA-NIC). Through a combination of NMR spectroscopy, molecular dynamics simulations, and nucleation studies using Crystal16, we evaluated the effects of polyethylene glycol (PEG), polyvinylpyrrolidone-vinyl acetate (PVP-VA), and soluplus (SOL), both individually and in combinations, on the nucleation, diffusion, and self-association of FFA molecules in solution. 1H NMR and DOSY measurements revealed that while PEG was highly effective in reducing molecular mobility, thus significantly delaying nucleation, PVP-VA facilitated nucleation by enhancing FFA diffusion and aggregation. SOL provided a balance, enhancing molecular mobility but maintaining a delayed nucleation effect, likely due to micellar encapsulation, as evidenced by line broadening in 1H NMR. Combination systems such as PVP-VA-PEG and PVP-VA-SOL showed synergistic effects, with PVP-VA-SOL proving particularly effective in inhibiting FFA nucleation across all systems. Molecular dynamics simulations supported these findings by highlighting changes in intermolecular interactions and aggregation tendencies in the presence of each polymer. This comprehensive analysis suggested that selecting appropriate polymeric excipients, or combinations thereof, can finely tune the nucleation behaviors of drug solutions, offering a strategic approach to optimizing the stability of supersaturated drug solutions.

Optimizing Andrographolide from Sambiloto Leaves (Andrographis paniculata) Using Cyclodextrin Metal-Organic Frameworks for Targeted Pulmonary Delivery via a Metered Dose Inhaler: A Proof-Of-Concept Study

Andrographis paniculata is recognized for its numerous applications in the pharmaceutical industry. The primary compound of this plant, andrographolide (AG), has demonstrated potent antibacterial properties, including against K. pneumoniae. However, its poor solubility limits its bioavailability. To address this, the creation of an inclusion complex (IC) using cyclodextrin (CD) and Metal-Organic Frameworks (MOFs) offers a promising solution for improving AG's solubility and bioavailability. The AG-CD-MOFs are intended to be delivered via a metered dose inhaler (MDI), allowing for direct targeting of lung tissue. This research focuses on designing AG encapsulated within CD-MOFs to boost solubility and enhance drug efficacy when delivered directly to the lungs via an MDI. Computational molecular modeling indicated that γ-CD is the most suitable host molecule for forming an inclusion complex (IC) with AG, surpassing α-CD and β-CD. The optimal AG to γ-CD ratio for the IC is 1:2 (w/w), with a particle size of 534.53 ± 49.11 nm, a PDI of 0.121 ± 0.01, an encapsulation efficiency (EE) of 89.45 ± 7.03%, and a drug loading (DL) of 26.09 ± 2.87%. The IC exhibits strong antibacterial activity comparable to AG crystal-DMSO, highlighting the importance of solubility in AG's antibacterial efficacy. Additionally, drug release studies revealed that the IC's release profile is nearly nine times greater than that of the AG crystal. In vivo studies further demonstrated the high selectivity of the MDI for lung tissue delivery compared to injection and oral administration, with drug concentrations of 7.44 ± 0.57 μg/mL, 1.52 ± 0.23 μg/mL, and 1.5 ± 0.16 μg/mL, respectively. Moreover, the MDI AG-CD-MOFs exhibited sustained-release properties, maintaining a drug concentration of 5.27 ± 0.75 μg/mL in lung tissue for up to 48 h, significantly higher than injection and oral administration, which only maintained concentrations of 1.52 ± 0.23 μg/mL and 1.50 ± 0.16 μg/mL at 8 h, respectively. The developed formulation shows high selectivity to lung tissue and shows sustained-release behavior. The formula was deemed safe based on in vitro hemolysis and irritation risk tests and did not cause inflammation in lung tissue, as confirmed by histopathology studies. Furthermore, in vivo studies are strongly recommended to validate this therapy and improve pneumonia treatment options.

Unraveling the Impact of Stabilizers on Nanocrystal Preparation and Oral Absorption: A Case Study of Poorly Soluble Andrographolide

Drug nanocrystal engineering is an attractive pharmaceutical approach to enhancing the oral bioavailability of poorly soluble drugs. The mechanism of drug nanocrystal stabilization, however, is unclear. Here we developed andrographolide nanocrystals (AG-NCs) with various nonionic surfactants (Pluronic-F127, TPGS, or Brij-S20). We detected AG micelles (AG-MCs) at an andrographolide to nonionic surfactant ratio of 10:10 (w/w) and poor AG-NC size stability. We thus quantified the unbound Pluronic-F127 in AG-NCs and found that the proposed instantaneous binding rate sharply declined with increasing Pluronic-F127 input. We determined that the saturation dose of TPGS on AG-NCs was approximately 10:10 (w/w) and recommend it as a key criterion for nanocrystal formulation. Although AG-NCs exhibited a marginally faster dissolution rate, they possessed better mucus-penetrating and transmembrane transport capacities and significantly enhanced oral absorption compared to AG-MCs. These findings give insights into the impact of a stabilizer during the preparation process and the oral absorption of drug nanocrystals.

Driving forces and large scale affinity calculations for piperine/γ-cyclodxetrin complexes: Mechanistic insights from umbrella sampling simulation and DFT calculations

Piperine (PiP), a bioactive molecule, exhibits numerous health benefits and is frequently employed as a co-delivery agent with various phytomedicines (e.g., curcumin) to enhance their bioavailability. This is attributed to PiP's inhibitory activity against drug-metabolizing proteins, notably CYP3A4. Nevertheless, PiP encounters solubility challenges addressed in this study using cyclodextrins (CDs). Specifically, γ-CD and its derivatives, Hydroxypropyl-γ-CD (HP-γ-CD), and Octakis (6-O-sulfo)-γ-CD (Octakis-S-γ-CD), were employed to form supramolecular complexes with PiP. The conformational space of the complexes was assessed through 1 μs molecular dynamics simulations and umbrella sampling. Additionally, quantum mechanical calculations using wB97X-D dispersion-corrected DFT functional and 6-311 + G(d,p) basis set were conducted on the complexes to examine the thermodynamics and kinetic stability. Results indicated that Octakis-S-γ-CD exhibits superior host capabilities for PiP, with the most favorable complexation energy (-457.05 kJ/mol), followed by HP-γ-CD (-249.16 kJ/mol). Furthermore, two conformations of the Octakis-S-γ-CD/PiP complex were explored to elucidate the optimal binding orientation of PiP within the binding pocket of Octakis-S-γ-CD. Supramolecular chemistry relies significantly on non-covalent interactions. Therefore, our investigation extensively explores the critical atoms involved in these interactions, elucidating the influence of substituted groups on the stability of inclusion complexes. This comprehensive analysis contributes to emphasizing the γ-CD derivatives with improved host capacity.

High-throughput preparation, scale up and solidification of andrographolide nanosuspension using hummer acoustic resonance technology

The aim of this study was to rapidly develop a sufficiently robust andrographolide nanosuspension (AG-NS) system using hummer acoustic resonance (HAR) technology. The system can effectively improve the dissolution properties of AG, while having high stability and scale-up adaptability. The formulation of AG-NS was optimized in a high-throughput manner using HAR technology and the preparation process was optimized stepwise. Optimal AG-NS with Z-Ave = 223.99 ± 3.16 nm, PDI=0.095 ± 0.007 and zeta potential = -33.20 ± 0.58 mV was successfully prepared with Polyvinylpyrrolidone K30 and Sodium dodecyl sulfate. The optimal prescription was successfully scaled up 100 and 150 times using HAR technology, which was the initial exploration of its commercial scale production. AG-NS was solidified using freeze drying and fluid bed technology, respectively. The optimal AG-NS and its solidified products were exhaustively characterized using various analytical techniques. The high energy input of HAR technology and drying process converted part of the drug into the amorphous state. The in-vitro drug dissolution studies demonstrated relatively higher drug dissolution for AG-NS and its solidified products compared to controls at both the dissolution media (pH 1.2 buffer and pH 6.8 buffer). AG-NS and its solidified products successfully maintained their physical stability in short-term stability and accelerated stability experiments, respectively.

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.