Integrated in silico formulation design of self-emulsifying drug delivery systems

Comparison of two self-nanoemulsifying drug delivery systems using different solidification techniques for enhanced solubility and oral bioavailability of poorly water-soluble celecoxib

In this study, we aimed to develop a solid self-nanoemulsifying drug delivery system (S-SNEDDS) and a solid self-nanoemulsifying granule system (S-SNEGS) to enhance the solubility and oral bioavailability of celecoxib. This process involved the preparation of a liquid SNEDDS (L-SNEDDS) and its subsequent solidification into a S-SNEDDS and a S-SNEGS. The L-SNEDDS consisted of celecoxib (drug), Captex® 355 (Captex; oil), Tween® 80 (Tween 80; surfactant) and D-α-Tocopherol polyethylene glycol 1000 succinate (TPGS; cosurfactant) in a weight ratio of 3.5:25:60:15 to produce the smallest nanoemulsion droplet size. The S-SNEDDS and S-SNEGS were prepared with L-SNEDDS/Ca-silicate/Avicel PH 101 in a weight ratio of 103.5:50:0 using a spray dryer and 103.5:50:100 using a fluid bed granulator, respectively. We compared the two novel developed systems and celecoxib powder based on their solubility, dissolution rate, physicochemical properties, flow properties and oral bioavailability in rats. S-SNEGS showed a significant improvement in solubility and dissolution rate compared to S-SNEDDS and celecoxib powder. Both systems had been converted from crystalline drug to amorphous form. Furthermore, S-SNEGS exhibited a significantly reduced angle of repose, compressibility index and Hausner ratio than S-SNEDDS, suggesting that S-SNEGS was significantly superior in flow properties. Compared to S-SNEDDS and celecoxib powder, S-SNEGS increased the oral bioavailability (AUC value) in rats by 1.3 and 4.5-fold, respectively. Therefore, S-SNEGS wolud be recommended as a solid self-nanoemulsifying system suitable for poorly water-soluble celecoxib.

Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion

(20 S)-Ginsenoside Rh2 is a natural saponin derived from Panax ginseng Meyer (P. ginseng), which showed significantly potent anticancer properties. However, its low water solubility and bioavailability strongly restrict its pharmaceutical applications. The aim of current research is to develop a modified (20 S)-Ginsenoside Rh2 formulation with high solubility, dissolution rate and bioavailability by combined computational and experimental methodology. The "PharmSD" model was employed to predict the optimal polymer for (20 S)-Ginsenoside Rh2 solid dispersion formulations. The solubility of (20 S)-Ginsenoside Rh2 in various polymers was assessed, and the optimal ternary solid dispersion was evaluated across different dissolution mediums. Characterization techniques included the Powder X-ray diffraction (PXRD) and Fourier transform infrared spectroscopy (FTIR). Molecular dynamics simulations were employed to elucidate the formation mechanism of the solid dispersion and the interactions among active pharmaceutical ingredient (API) and excipient molecules. Cell and animal experiments were conducted to evaluate the in vivo performance of the modified formulation. The "PharmSD" solid dispersion model identified Gelucire 44/14 as the most effective polymer for enhancing the dissolution rate of Rh2. Subsequent experiment also confirmed that Gelucire 44/14 outperformed the other selected polymers. Moreover, the addition of the third component, sodium dodecyl sulfate (SDS), in the ternary solid dispersion formulation significantly amplified dissolution rates than the binary systems. Characterization experiments revealed that the API existed in an amorphous state and interacted via hydrogen bonding with SDS and Gelucire. Moreover, molecular modeling results provided additional evidence of hydrogen bonding interactions between the API and excipient molecules within the optimal ternary solid dispersion. Cell experiments demonstrated efflux ratio (EfR) of Rh2 ternary solid dispersion was lower than that of pure Rh2. In vivo experiments revealed that the modified formulation substantially improved the absorption of Rh2 in rats. Our research successfully developed an optimal ternary solid dispersion for Rh2 with high solubility, dissolution rate and bioavailability by integrated computational and experimental tools. The combination of Artificial Intelligence (AI) technology and molecular dynamics simulation is a wise way to support the future formulation development.

Solubility enhancement of fexofenadine using self-nano emulsifying drug delivery system for improved biomimetic attributes

BACKGROUND

Fexofenadine is a poorly water-soluble drug, which limit its bioavailability and ultimately therapeutic efficacy. Liquid self-nano emulsifying drug delivery system (L-SNEDDs) is an approach that can enhance the solubility of fexofenadine by increasing its surface area and reducing the particle size, which increases the rate and extent of drug dissolution.

METHOD

In this investigation, L-SNEDDs of fexofenadine was made up using surfactants and co-surfactant. The SNEDDS formulation was optimized using a pseudo-ternary phase diagram and characterized.

RESULTS

The optimized L-SNEDDS incorporated fexofenadine were thermodynamically stable and showed mean droplet size and zeta potential of 155nm and -18mV, respectively unaffected by the media pH. In addition, the viscosity, and refractive index were observed 18.4 and 1.49 cps, respectively for optimized L-SNEDDS fortified fexofenadine. The results of Fourier transform infrared spectroscopy revealed an insignificant interaction between the fexofenadine and excipients. A drug loading efficiency of 94.20% resulted with a complete in vitro drug release in 2h, compared with the pure drug, which demonstrate significant improvement in the efficacy. Moreover, these results signify that on further in vivo assessment L-SNEDDS fortified fexofenadine can indicate improvement in pharmacokinetic and clinical outcome.

CONCLUSION

Thus, the investigation revealed that, the L-SNEDDs incorporated fexofenadine was most effective with a mixture of surfactant and co-surfactant with improved solubility intend to relieve pain associated with inflammation with single-dose oral administration.

Oral delivery of posaconazole-loaded phospholipid-based nanoformulation: Preparation and optimization using design of experiments, machine learning, and TOPSIS

Phospholipid-based nanosystems show promising potentials for oral administration of hydrophobic drugs. The study introduced a novel approach to optimize posaconazole-loaded phospholipid-based nanoformulation using the design of experiments, machine learning, and Technique for Order of Preference by Similarity to the Ideal Solution. These approaches were used to investigate the impact of various variables on the encapsulation efficiency (EE), particle size, and polydispersity index (PDI). The optimized formulation, with %EE of ∼ 74 %, demonstrated a particle size and PDI of 107.7 nm and 0.174, respectively. The oral pharmacokinetic profiles of the posaconazole suspension, empty nanoformulation + drug suspension, and drug-loaded nanoformulation were evaluated. The nanoformulation significantly increased maximum plasma concentration and the area under the drug plasma concentration-time curve (∼3.9- and 6.2-fold, respectively) and could be administered without regard to meals. MTT and histopathological examinations were carried out to evaluate the safety of the nanoformulation and results exhibited no significant toxicity. Lymphatic transport was found to be the main mechanism of oral delivery. Caco-2 cell studies demonstrated that the mechanism of delivery was not based on an increase in cellular uptake. Our study represents a promising strategy for the development of phospholipid-based nanoformulations as efficient and safe oral delivery systems.

Design and Evaluation of Ginkgolides Gastric Floating Controlled Release Tablets Based on Solid Supersaturated Self-nanoemulsifying

Ginkgolides are receptor antagonist of platelet activating factor with great clinical prospect, but its application is limited by its low solubility, short half-life and poor alkaline environment stability. It is difficult to solve these problems with a single drug delivery system. In this study, supersaturated self-nanoemulsifying gastric floating tablets of ginkgolides were developed through the combination of solid supersaturated self-nanoemulsifying drug delivery system (solid S-SNEDDS) and gastric retentive floating drug delivery system (GFDDS) to solve these problems of ginkgolides. Solid S-SNEDDS was prepared by D-optimal mixture design, normalization method and single factor experiment. The properties of solid-S-SNEDDS were studied by TEM, PXRD, FT-IR, SEM and in vitro drug release profile. Then, the optimal formulation of stomach floating tablet was obtained through single factor experiment and center composite design, followed by the study of in vitro release, model and mechanism of release, in vitro buoyancy and kinetics of erosion and swelling. PXRD and FT-IR showed that the drug in solid S-SNEDDS existed in an amorphous manner and formed hydrogen bond with excipients. The results showed that the cumulative release of GA and GB in the optimal tablets was 96.12% and 92.57% higher than the simple tablets within 12 h. The release mechanism of the tablet was skeleton erosion and drug diffusion. In 12 h, the optimal tablets can float stably in vitro and release the drug at a constant rate, with a cumulative release of more than 80%. In summary, the combination of SNEDDS and GFDDS is a promising means to solve the problems of ginkgolides.

A dataset of formulation compositions for self-emulsifying drug delivery systems

Self-emulsifying drug delivery systems (SEDDS) are a well-established formulation strategy for improving the oral bioavailability of poorly water-soluble drugs. Traditional development of these formulations relies heavily on empirical observation to assess drug and excipient compatibility, as well as to select and optimize the formulation compositions. The aim of this work was to leverage previously developed SEDDS in the literature to construct a comprehensive SEDDS dataset that can be used to gain insights and advance data-driven approaches to formulation development. A dataset comprised of 668 unique SEDDS formulations encompassing 20 poorly water-soluble drugs was curated. While there are still opportunities to enhance the quality and quantity of data on SEDDS, this research lays the groundwork to potentially simplify the SEDDS formulation development process.

FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence

Today, pharmaceutical industry faces great pressure to employ more efficient and systematic ways in drug discovery and development process. However, conventional formulation studies still strongly rely on personal experiences by trial-and-error experiments, resulting in a labor-consuming, tedious and costly pipeline. Thus, it is highly required to develop intelligent and efficient methods for formulation development to keep pace with the progress of the pharmaceutical industry. Here, we developed a comprehensive web-based platform (FormulationAI) for in silico formulation design. First, the most comprehensive datasets of six widely used drug formulation systems in the pharmaceutical industry were collected over 10 years, including cyclodextrin formulation, solid dispersion, phospholipid complex, nanocrystals, self-emulsifying and liposome systems. Then, intelligent prediction and evaluation of 16 important properties from the six systems were investigated and implemented by systematic study and comparison of different AI algorithms and molecular representations. Finally, an efficient prediction platform was established and validated, which enables the formulation design just by inputting basic information of drugs and excipients. FormulationAI is the first freely available comprehensive web-based platform, which provides a powerful solution to assist the formulation design in pharmaceutical industry. It is available at https://formulationai.computpharm.org/.

Revolutionizing drug formulation development: The increasing impact of machine learning

Over the past few years, the adoption of machine learning (ML) techniques has rapidly expanded across many fields of research including formulation science. At the same time, the use of lipid nanoparticles to enable the successful delivery of mRNA vaccines in the recent COVID-19 pandemic demonstrated the impact of formulation science. Yet, the design of advanced pharmaceutical formulations is non-trivial and primarily relies on costly and time-consuming wet-lab experimentation. In 2021, our group published a review article focused on the use of ML as a means to accelerate drug formulation development. Since then, the field has witnessed significant growth and progress, reflected by an increasing number of studies published in this area. This updated review summarizes the current state of ML directed drug formulation development, introduces advanced ML techniques that have been implemented in formulation design and shares the progress on making self-driving laboratories a reality. Furthermore, this review highlights several future applications of ML yet to be fully exploited to advance drug formulation research and development.

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.

Predicting liposome formulations by the integrated machine learning and molecular modeling approaches

Liposome is one of the most widely used carriers for drug delivery because of the great biocompatibility and biodegradability. Due to the complex formulation components and preparation process, formulation screening mostly relies on trial-and-error process with low efficiency. Here liposome formulation prediction models have been built by machine learning (ML) approaches. The important parameters of liposomes, including size, polydispersity index (PDI), zeta potential and encapsulation, are predicted individually by optimal ML algorithm, while the formulation features are also ranked to provide important guidance for formulation design. The analysis of key parameter reveals that drug molecules with logS [-3, -6], molecular complexity [500, 1000] and XLogP3 (≥2) are priority for preparing liposome with higher encapsulation. In addition, naproxen (NAP) and palmatine HCl (PAL) represented the insoluble and water-soluble molecules are prepared as liposome formulations to validate prediction ability. The consistency between predicted and experimental value verifies the satisfied accuracy of ML models. As the drug properties are critical for liposome particles, the molecular interactions and dynamics of NAP and PAL liposome are further investigated by coarse-grained molecular dynamics simulations. The modeling structure reveals that NAP molecules could distribute into lipid layer, while most PAL molecules aggregate in the inner aqueous phase of liposome. The completely different physical state of NAP and PAL confirms the importance of drug properties for liposome formulations. In summary, the general prediction models are built to predict liposome formulations, and the impacts of key factors are analyzed by combing ML with molecular modeling. The availability and rationality of these intelligent prediction systems have been proved in this study, which could be applied for liposome formulation development in the future.

Machine learning in accelerating microsphere formulation development

Microspheres have gained much attention from pharmaceutical and medical industry due to the excellent biodegradable and long controlled-release characteristics. However, the drug release behavior of microspheres is influenced by complicated formulation and manufacturing factors. The traditional formulation development of microspheres is intractable and inefficient by the experimentally trial-and-error methods. This research aims to build a prediction model to accelerate microspheres product development for small-molecule drugs by machine learning (ML) techniques. Two hundred eighty-six microsphere formulations with small-molecule drugs were collected from the publications and pharmaceutical company, including the dissolution temperature at both 37 ℃ and 45 ℃. After the comparison of fourteen ML approaches, the consensus model achieved accurate predictions for the validation set at 37 ℃ and 45 ℃ (R² = 0.880 vs. R² = 0.958), indicating the good performance to predict the in vitro drug release profiles at both 37 ℃ and 45 ℃. Meanwhile, the models revealed the feature importance of formulations, which offered meaningful insights to the microspheres development. Experiments of microsphere formulations further validated the accuracy of the consensus model. Furthermore, molecular dynamics (MD) simulation provided a microscopic view of the preparation process of microspheres. In conclusion, the prediction model of microsphere formulations for small-molecule drugs was successfully built with high accuracy, which is able to accelerate microspheres product development and promote the quality control of microspheres for the pharmaceutical industry.

How can machine learning and multiscale modeling benefit ocular drug development?

The eyes possess sophisticated physiological structures, diverse disease targets, limited drug delivery space, distinctive barriers, and complicated biomechanical processes, requiring a more in-depth understanding of the interactions between drug delivery systems and biological systems for ocular formulation development. However, the tiny size of the eyes makes sampling difficult and invasive studies costly and ethically constrained. Developing ocular formulations following conventional trial-and-error formulation and manufacturing process screening procedures is inefficient. Along with the popularity of computational pharmaceutics, non-invasive in silico modeling & simulation offer new opportunities for the paradigm shift of ocular formulation development. The current work first systematically reviews the theoretical underpinnings, advanced applications, and unique advantages of data-driven machine learning and multiscale simulation approaches represented by molecular simulation, mathematical modeling, and pharmacokinetic (PK)/pharmacodynamic (PD) modeling for ocular drug development. Following this, a new computer-driven framework for rational pharmaceutical formulation design is proposed, inspired by the potential of in silico explorations in understanding drug delivery details and facilitating drug formulation design. Lastly, to promote the paradigm shift, integrated in silico methodologies were highlighted, and discussions on data challenges, model practicality, personalized modeling, regulatory science, interdisciplinary collaboration, and talent training were conducted in detail with a view to achieving more efficient objective-oriented pharmaceutical formulation design.

Molecular Design of Interfaces of Model Food Nanoemulsions: A Combined Experimental and Theoretical Approach

The composition and structure of the interfacial region of emulsions frequently determine its functionality and practical applications. In this work, we have integrated theory and experiments to enable a detailed description of the location and orientation of antioxidants in the interfacial region of olive-oil-in-water nanoemulsions (O/W) loaded with the model gallic acid (GA) antioxidant. For the purpose, we determined the distribution of GA in the intact emulsions by employing the well-developed pseudophase kinetic model, as well as their oxidative stability. We also determined, by employing an in silico design, the radial distribution functions of GA to gain insights on its insertion depth and on its orientation in the interfacial region. Both theoretical and experimental methods provide comparable and complementary results, indicating that most GA is located in the interfacial region (~81.2%) with a small fraction in the aqueous (~18.82%). Thus, GA is an effective antioxidant to inhibit lipid oxidation in emulsions not only because of the energy required for its reaction with peroxyl radical is much lower than that between the peroxyl radical and the unsaturated lipid but also because its effective concentration in the interfacial region is much higher than the stoichiometric concentration. The results demonstrate that the hybrid approach of experiments and simulations constitutes a complementary and useful pathway to design new, tailored, functionalized emulsions to minimize lipid oxidation.

Application of Design of Experiments in the Development of Self-Microemulsifying Drug Delivery Systems

Oral delivery has become the route of choice among all other types of drug administrations. However, typical chronic disease drugs are often poorly water-soluble, have low dissolution rates, and undergo first-pass metabolism, ultimately leading to low bioavailability and lack of efficacy. The lipid-based formulation offers tremendous benefits of using versatile excipients and has great compatibility with all types of dosage forms. Self-microemulsifying drug delivery system (SMEDDS) promotes drug self-emulsification in a combination of oil, surfactant, and co-surfactant, thereby facilitating better drug solubility and absorption. The feasible preparation of SMEDDS creates a promising strategy to improve the drawbacks of lipophilic drugs administered orally. Selecting a decent mixing among these components is, therefore, of importance for successful SMEDDS. Quality by Design (QbD) brings a systematic approach to drug development, and it offers promise to significantly improve the manufacturing quality performance of SMEDDS. Furthermore, it could be benefited efficiently by conducting pre-formulation studies integrated with the statistical design of experiment (DoE). In this review, we highlight the recent findings for the development of microemulsions and SMEDDS by using DoE methods to optimize the formulations for drugs in different excipients with controllable ratios. A brief overview of DoE concepts is discussed, along with its technical benefits in improving SMEDDS formulations.

Highly Accessible Computational Prediction and In Vivo/In Vitro Experimental Validation: Novel Synthetic Phenyl Ketone Derivatives as Promising Agents against NAFLD via Modulating Oxidoreductase Activity

Nonalcoholic fatty liver disease (NAFLD) has reached epidemic proportions with no pharmacological treatment approved. Several highly accessible computational tools were employed to predict the activities of twelve novel compounds prior to actual chemical synthesis. We began our work by designing two or three hydroxyl groups appended to the phenyl ketone core, followed by prediction of drug-likeness and targets. Most predicted targets for each compound overlapped with NAFLD targets (≥80%). Enrichment analysis showed that these compounds might regulate oxidoreductase activity. Then, these compounds were synthesized and confirmed by IR, MS, ¹H, and ¹³C NMR. Their cell viability demonstrated that twelve compounds exhibited appreciable potencies against NAFLD (EC₅₀ values ≤ 13.5 μM). Furthermore, the most potent compound 5f effectively prevented NAFLD progression as evidenced by the change in histological features. 5f significantly reduced total cholesterol and triglyceride levels in vitro/in vivo, and the effects of 5f were significantly stronger than those of the control drug. The proteomic data showed that oxidoreductase activity was the most significantly enriched, and this finding was consistent with docking results. In summary, this validated presynthesis prediction approach was cost-saving and worthy of popularization. The novel synthetic phenyl ketone derivative 5f holds great therapeutic potential by modulating oxidoreductase activity to counter NAFLD.

In silico-assisted development of supersaturable preconcentrated isotropic mixture of atazanavir for augmenting biopharmaceutical performance in the presence of H2-receptor antagonist

The therapeutic potential of atazanavir (BCS Class II drug), a highly selective inhibitor of human immunodeficiency virus (HIV-1), has been largely limited due to its low intrinsic solubility at elevated pH resulting in low oral bioavailability. Thus, the current work describes the systematic development, optimization, and evaluation of hydroxypropyl methylcellulose acetate succinate (HPMC-AS)-based supersaturable preconcentrate isotropic mixture (SP-IM) containing long-chain triglyceride to improve intestinal lymphatic transport and augment oral bioavailability of atazanavir (ATZ). A D-optimal mixture design was employed for optimization of plain IM containing corn oil, oleic acid, Tween 80, and propylene glycol, evaluating various critical quality attributes (CQAs) like particle size, polydispersity index, self-emulsification time, % transmittance, and drug content. In silico analysis and in vitro supersaturation test facilitated the selection of HPMC-AS as a best suited polymeric precipitation inhibitor (PPI) for formulating ATZ loaded SP-IM (ATZ-SP-IM). In vitro dissolution data indicated that ATZ-SP-IM exhibits superior performance in 0.025 N HCl and pH 6.8 over pure drug. Ex vivo permeation and in vivo pharmacokinetic study of ATZ-SP-IM corroborated enhanced permeation (2.03 fold) and improved drug absorption via lymphatic transport in Wistar rats. Further, the pharmacokinetic performance of ATZ-SP-IM was not affected in presence of H₂ receptor antagonist. Therefore, the results showed that ATZ-SP-IM can significantly improve the biopharmaceutical attributes of ATZ so as to lay a foundation of further research on the new dosage form of ATZ.

Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines?

Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.

Machine Learning-Enabled NIR Spectroscopy in Assessing Powder Blend Uniformity: Clear-Up Disparities and Biases Induced by Physical Artefacts

NIR spectroscopy is a non-destructive characterization tool for the blend uniformity (BU) assessment. However, NIR spectra of powder blends often contain overlapping physical and chemical information of the samples. Deconvoluting the information related to chemical properties from that associated with the physical effects is one of the major objectives of this work. We achieve this aim in two ways. Firstly, we identified various sources of variability that might affect the BU results. Secondly, we leverage the machine learning-based sophisticated data analytics processes. To accomplish the aforementioned objectives, calibration samples of amlodipine as an active pharmaceutical ingredient (API) with the concentrations ranging between 67 and 133% w/w (dose ~ 3.6% w/w), in powder blends containing excipients, were prepared using a gravimetric approach and assessed using NIR spectroscopic analysis, followed by HPLC measurements. The bias in NIR results was investigated by employing data quality metrics (DQM) and bias-variance decomposition (BVD). To overcome the bias, the clustered regression (non-parametric and linear) was applied. We assessed the model's performance by employing the hold-out and k-fold internal cross-validation (CV). NIR-based blend homogeneity with low mean absolute error and an interval estimates of 0.674 (mean) ± 0.218 (standard deviation) w/w was established. Additionally, bootstrapping-based CV was leveraged as part of the NIR method lifecycle management that demonstrated the mean absolute error (MAE) of BU ± 3.5% w/w and BU ± 1.5% w/w for model generalizability and model transferability, respectively. A workflow integrating machine learning to NIR spectral analysis was established and implemented. Impact of various data learning approaches on NIR spectral data.

Improved oral bioavailability of poorly water-soluble vorinostat by self-microemulsifying drug delivery system

Background

Vorinostat is a histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) with anticancer properties. However, it is plagued by low water solubility, low permeability (BCS class IV drug), and suboptimal pharmacokinetics. The purpose of the present study was to develop a self-microemulsifying drug delivery system (SMEDDS) to enhance the oral bioavailability of vorinostat. Capryol 90, labrasol, and polyethylene glycol (PEG 400) were selected as oil phase, surfactant, and co-surfactant, respectively. The vorinostat self-microemulsifying drug delivery systems were tested for self-microemulsifying time, phase separation, effect of pH, droplet size, zeta potential, dilution study, Fourier-transform infrared (FT-IR) spectroscopy analysis, and field emission scanning electron microscopy (FESEM). A rat model in vivo pharmacokinetic study was conducted for the optimized formulation against vorinostat pure drug powder.

Results

The results from the characterization studies showed that the optimized formulation (F7) self-microemulsification time was 1.4 ± 0.05 min and no precipitation or phase separation was observed. The mean droplet size, polydispersity index (PDI), and zeta potential of the optimized formulation (F7) were found to be 272.9 ± 82.7 nm, 0.415, and − 57.2 mV, respectively. The pharmacokinetic parameters of the optimized formulation (F7) showed a 1.6-fold increase in maximum concentration (Cmax) and a 3.6-fold increase in area under the curve (AUC(0−∞)), in comparison with pure drug in suspension.

Conclusions

The findings suggest that SMEDDS formulation could be an effective method for increasing the oral bioavailability of vorinostat, which is poorly water soluble.

Fundamental Aspects of Lipid-Based Excipients in Lipid-Based Product Development

Poor aqueous solubility of drugs is still a foremost challenge in pharmaceutical product development. The use of lipids in designing formulations provides an opportunity to enhance the aqueous solubility and consequently bioavailability of drugs. Pre-dissolution of drugs in lipids, surfactants, or mixtures of lipid excipients and surfactants eliminate the dissolution/dissolving step, which is likely to be the rate-limiting factor for oral absorption of poorly water-soluble drugs. In this review, we exhaustively summarize the lipids excipients in relation to their classification, absorption mechanisms, and lipid-based product development. Methodologies utilized for the preparation of solid and semi-solid lipid formulations, applications, phase behaviour, and regulatory perspective of lipid excipients are discussed.

Computational and Experimental Models of Type III Lipid-Based Formulations of Loratadine Containing Complex Nonionic Surfactants

Type III lipid-based formulations (LBFs) combine poorly water-soluble drugs with oils, surfactants, and cosolvents to deliver the drugs into the systemic circulation. However, the solubility of the drug can be influenced by the colloidal phases formed in the gastrointestinal tract as the formulation is dispersed and makes contact with bile and other materials present within the GI tract. Thus, an understanding of the phase behavior of LBFs in the gut is critical for designing efficient LBFs. Molecular dynamics (MD) simulation is a powerful tool for the study of colloidal systems. In this study, we modeled the internal structures of five type III LBFs of loratadine containing poly(ethylene oxide) nonionic surfactants polysorbate 80 and polyoxyl hydrogenated castor oil (Kolliphor RH40) using long-timescale MD simulations (0.4-1.7 μs). We also conducted experimental investigations (dilution of formulations with water) including commercial Claritin liquid softgel capsules. The simulations show that LBFs form continuous phase, water-swollen reverse micelles, and bicontinuous and phase-separated systems at different dilutions, which correlate with the experimental observations. This study supports the use of MD simulation as a predictive tool to determine the fate of LBFs composed of medium-chain lipids, polyethylene oxide surfactants, and polymers.

Prediction of lipid nanoparticles for mRNA vaccines by the machine learning algorithm

Lipid nanoparticle (LNP) is commonly used to deliver mRNA vaccines. Currently, LNP optimization primarily relies on screening ionizable lipids by traditional experiments which consumes intensive cost and time. Current study attempts to apply computational methods to accelerate the LNP development for mRNA vaccines. Firstly, 325 data samples of mRNA vaccine LNP formulations with IgG titer were collected. The machine learning algorithm, lightGBM, was used to build a prediction model with good performance (R² > 0.87). More importantly, the critical substructures of ionizable lipids in LNPs were identified by the algorithm, which well agreed with published results. The animal experimental results showed that LNP using DLin-MC3-DMA (MC3) as ionizable lipid with an N/P ratio at 6:1 induced higher efficiency in mice than LNP with SM-102, which was consistent with the model prediction. Molecular dynamic modeling further investigated the molecular mechanism of LNPs used in the experiment. The result showed that the lipid molecules aggregated to form LNPs, and mRNA molecules twined around the LNPs. In summary, the machine learning predictive model for LNP-based mRNA vaccines was first developed, validated by experiments, and further integrated with molecular modeling. The prediction model can be used for virtual screening of LNP formulations in the future.

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.

Molecular Dynamics Simulations and Experimental Results Provide Insight into Clinical Performance Differences between Sandimmune® and Neoral® Lipid-Based Formulations

OBJECTIVE

Molecular dynamics (MD) simulations provide an in silico method to study the structure of lipid-based formulations (LBFs) and the incorporation of poorly water-soluble drugs within such formulations. In order to validate the ability of MD to effectively model the properties of LBFs, this work investigates the well-known cyclosporine A formulations, Sandimmune® and Neoral®. Sandimmune® exhibits poor dispersibility and its absorption from the gastrointestinal tract is enhanced when administered after food, whereas Neoral® disperses comparatively well and shows no food effect.

METHODS

MD simulations were performed of both LBFs to investigate the differences observed in fasted and fed conditions. These conditions were also tested using an in vitro experimental model of dispersion and digestion.

RESULTS

These MD simulations were able to show that the food effect observed for Sandimmune® can be explained by large changes in drug solubilization on addition of bile. In contrast, Neoral® is well dispersed in water or in simulated fasted conditions, and this dispersion is relatively unchanged on moving to fed conditions. These differences were confirmed using dispersion and digestion in vitro experimental model.

CONCLUSIONS

The current data suggests that MD simulations are a potential method to model the fate of LBFs in the gastrointestinal tract, predict their dispersion and digestion, investigate behaviour of APIs within the formulations, and provide insights into the clinical performance of LBFs.