Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development.

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/.

Predicting Peptide Permeability Across Diverse Barriers: A Systematic Investigation.

Peptide-based therapeutics hold immense promise for the treatment of various diseases. However, their effectiveness is often hampered by poor cell membrane permeability, hindering targeted intracellular delivery and oral drug development. This study addressed this challenge by introducing a novel graph neural network (GNN) framework and advanced machine learning algorithms to build predictive models for peptide permeability. Our models offer systematic evaluation across diverse peptides (natural, modified, linear and cyclic) and cell lines [Caco-2, Ralph Russ canine kidney (RRCK) and parallel artificial membrane permeability assay (PAMPA)]. The predictive models for linear and cyclic peptides in Caco-2 and RRCK cell lines were constructed for the first time, with an impressive coefficient of determination (R2) of 0.708, 0.484, 0.553, and 0.528 in the test set, respectively. Notably, the GNN framework behaved better in permeability prediction with larger data sets and improved the accuracy of cyclic peptide prediction in the PAMPA cell line. The R2 increased by about 0.32 compared with the reported models. Furthermore, the important molecular structural features that contribute to good permeability were interpreted; the influence of cell lines, peptide modification, and cyclization on permeability were successfully revealed. To facilitate broader use, we deployed these models on the user-friendly KNIME platform (https://github.com/ifyoungnet/PharmPapp). This work provides a rapid and reliable strategy for systematically assessing peptide permeability, aiding researchers in drug delivery optimization, peptide preselection during drug discovery, and potentially the design of targeted peptide-based materials.

Self-Assembled Micelles Based on Ginsenoside Rg5 for the Targeted Treatment of PTX-Resistant Tumors.

Paclitaxel (PTX) is one of the first-line drugs for prostate cancer (PC) treatment. However, the poor water solubility, inadequate specific targeting ability, multidrug resistance, and severe neurotoxicity are far from being fully resolved, despite diverse PTX formulations in the market, such as the gold-standard PTX albumin nanoparticle (Abraxane) and polymer micelles (Genexol-PM). Some studies attempting to solve the multiple problems of chemotherapy delivery fall into the trap of an extremely complicated formulation design and sacrifice druggability. To better address these issues, this study designed an efficient, toxicity-reduced paclitaxel-ginsenoside polymeric micelle (RPM). With the aid of the inherent amphiphilic molecular structure and pharmacological effects of ginsenoside Rg5, the prepared RPM enhances the water solubility and active targeting of PTX, inhibiting chemotherapy resistance in cancer cells. Moreover, the polymeric micelles demonstrated favorable anti-inflammatory and neuroprotective effects, providing ideas for the development of new clinical anti-PC preparations.

PBPK/PD Modeling of Nifedipine for Precision Medicine in Pregnant Women: Enhancing Clinical Decision-Making for Optimal Drug Therapy.

OBJECTIVE: This study aims to develop physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) predictive models for nifedipine in pregnant women, enhancing precision medicine and reducing adverse reactions for both mothers and infants. METHODS: A PBPK/PD model was constructed using PK-Sim, MoBi, and MATLAB software, integrating literature and pregnancy-specific physiological information. The process involved: (1) establishing and validating a PBPK model for serum clearance after intravenous administration in non-pregnant individuals, (2) establishing and validating a PBPK model for serum clearance after oral administration in non-pregnant individuals, (3) constructing and validating a PBPK model for enzyme clearance after oral administration in non-pregnant individuals, and (4) adjusting the PBPK model structure and enzyme parameters according to pregnant women and validating it in oral administration. (5) PK/PD model was explored through MATLAB, and the PBPK and PK/PD models were integrated to form the PBPK/PD model. RESULTS: The Nifedipine PBPK model's predictive accuracy was confirmed by non-pregnant and pregnant validation studies. The developed PBPK/PD model accurately predicted maximum antihypertensive effects for clinical doses of 5, 10, and 20 mg. The model suggested peak effect at 0.86 h post-administration, achieving blood pressure reductions of 5.4 mmHg, 14.3 mmHg, and 21.3 mmHg, respectively. This model provides guidance for tailored dosing in pregnancy-induced hypertension based on targeted blood pressure reduction. CONCLUSION: Based on available literature data, the PBPK/PD model of Nifedipine in pregnancy demonstrated good predictive performance. It will help optimize individualized dosing of Nifedipine, improve treatment outcomes, and minimize the risk of adverse reactions in mothers and infants.

A Comprehensive Analysis of Biopharmaceutical Products Listed in the FDA's Purple Book.

Although biopharmaceuticals constitute around 10% of the drug landscape, eight of the ten top-selling products were biopharmaceuticals in 2023. This study did a comprehensive analysis of the FDA's Purple Book database. Firstly, our research uncovered market trends and provided insights into biologics distributions. According to the investigation, although biotechnology has advanced and legislative shifts have made the approval process faster, there are still challenges to overcome, such as molecular instability and formulation design. Moreover, our research comprehensively analyzed biological formulations, pointing out significant strategies regarding administration routes, dosage forms, product packaging, and excipients. In conjunction with biologics, the widespread integration of innovative delivery strategies will be implemented to confront the evolving challenges in healthcare and meet an expanding array of treatment needs.

Physiologically Based Pharmacokinetic Modeling for Multiple Oral Administration Labetalol in Pregnant Women.

BACKGROUND: Labetalol has an irreplaceable role in treating Hypertensive disorders of pregnancy (HDP), a common disease during pregnancy with a prevalence of 5.2-8.2%. However, there were big differences in dosage regimens between various guidelines. PURPOSE: A physiologically-based pharmacokinetics (PBPK) model was established and validated to evaluate the existing oral dosage regimens, and to compare the difference in plasma concentration between pregnant and non-pregnant women. METHODS: First, non-pregnant woman models with specific plasma clearance or enzymatic metabolism (UGT1A1, UGT2B7, CYP2C19) were established and validated. For CYP2C19, slow, intermediate, and rapid metabolic phenotypes were considered. Then, a pregnant model with proper structure and parameters adjustment was established and validated against the multiple oral administration data. RESULTS: The predicted labetalol exposure captured the experimental data well. The following simulations with criteria lowering 15 mmHg blood pressure (corresponding to around 108 ng/ml plasma labetalol) found that the maximum daily dosage in the Chinese guideline may be insufficient for some severe HDP patients. Moreover, similar predicted steady-state trough plasma concentration was found between the maximum daily dosage in the American College of Obstetricians and Gynecologists (ACOG) guideline, 800 mg Q8h and a regimen of 200 mg Q6h. Simulations comparing non-pregnant and pregnant women found that the difference in labetalol exposure highly depended on the CYP2C19 metabolic phenotype. CONCLUSIONS: In summary, this work initially established a PBPK model for multiple oral administration of labetalol for pregnant women. This PBPK model may lead to personalized labetalol medication in the future.

Predicting Glass-Forming Ability of Pharmaceutical Compounds by Using Machine Learning Technologies.

Low aqueous solubility is a common and serious challenge for most drug substances not only in development but also in the market, and it may cause low absorption and bioavailability as a result. Amorphization is an intermolecular modification strategy to address the issue by breaking the crystal lattice and enhancing the energy state. However, due to the physicochemical properties of the amorphous state, drugs are thermodynamically unstable and tend to recrystallize over time. Glass-forming ability (GFA) is an experimental method to evaluate the forming and stability of glass formed by crystallization tendency. Machine learning (ML) is an emerging technique widely applied in pharmaceutical sciences. In this study, we successfully developed multiple ML models (i.e., random forest (RF), XGBoost, and support vector machine (SVM)) to predict GFA from 171 drug molecules. Two different molecular representation methods (i.e., 2D descriptor and Extended-connectivity fingerprints (ECFP)) were implemented to process the drug molecules. Among all ML algorithms, 2D-RF performed best with the highest accuracy, AUC, and F1 of 0.857, 0.850, and 0.828, respectively, in the testing set. In addition, we conducted a feature importance analysis, and the results mostly agreed with the literature, which demonstrated the interpretability of the model. Most importantly, our study showed great potential for developing amorphous drugs by in silico screening of stable glass formers.

Prediction of Free Drug Absorption in Cyclodextrin Formulation by a Modified Physiologically Based Pharmacokinetic Model and Phase Solubility 3-D Surface Graph.

PURPOSE: Cyclodextrin (CD) is commonly used to enhance the solubility of oral drugs. However, with the increase of CD concentrations, the fraction of free drug molecules decreases, which may potentially impede drug absorption. This study aims to predict the optimal ratio between drug and CD to achieve the best absorption efficiency by computational simulation. METHODS: First, a physiologically based pharmacokinetic (PBPK) model was developed. This model can continuously adjust absorption according to free drug fraction and was validated against two model drugs, progesterone (PG) and andrographolide (AG). The further analysis involves 3-D surface graphs to investigate the relationship between free drug amount, theoretically absorbable concentration, and contents of drug and CD in the formulation. RESULTS: The PBPK model predicted the PK behavior of two drugs well. The concentration ratio of drug to CD, leading to maximal free drug amount and the best absorption efficiency, is nearly the same as the slope determined in the phase solubility test. The new modified PBPK model and 3-D surface graph can easily predict the absorption difference of formulations with various drug/CD ratios. CONCLUSION: This PBPK model and 3-D surface graph can predict the absorption and determine the optimal concentration ratio of CD formulation, which could accelerate the R&D of CD formulation.

Quercetin in Animal Models of Alzheimer's Disease: A Systematic Review of Preclinical Studies.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. It involves progressive impairment of cognitive function. A growing number of neuroprotective compounds have been identified with potential anti-AD properties through in vitro and in vivo models of AD. Quercetin, a natural flavonoid contained in a wide range of plant species, is repeatedly reported to exert neuroprotective effects in experimental animal AD models. However, a systematic analysis of methodological rigor and the comparison between different studies is still lacking. A systematic review uses a methodical approach to minimize the bias in each independent study, providing a less biased, comprehensive understanding of research findings and an objective judgement of the strength of evidence and the reliability of conclusions. In this review, we identified 14 studies describing the therapeutic efficacy of quercetin on animal AD models by electronic and manual retrieval. Some of the results of the studies included were meta-analyzed by forest plot, and the methodological quality of each preclinical trial was assessed with SYRCLE's risk of bias tool. Our results demonstrated the consistent neuroprotective effects of quercetin on different AD models, and the pharmacological mechanisms of quercetin on AD models are summarized. This information eliminated the bias of each individual study, providing guidance for future tests and supporting evidence for further implementation of quercetin into clinical trials. However, the limitations of some studies, such as the absence of sample size calculations and low method quality, should also be noted.

Development of a Highly Water-Soluble Lycopene Cyclodextrin Ternary Formulation by the Integrated Experimental and Modeling Techniques.

Lycopene, the aliphatic hydrocarbon carotenoid with abundant bioactivities, has instability, extremely poor water solubility, and low oral bioavailability. The study aimed to develop a highly water-soluble and practical lycopene formulation to improve the oral bioavailability and efficiency of lycopene. Environment-friendly hot-melt extrusion (HME) technique was applied to fabricate lycopene-cyclodextrin-polyethylene glycol 6000 (PEG 6000) ternary systems, which possessed highly aqueous solubility (897.665 µg mL-1), almost 32-fold higher than that of the reported lycopene binary inclusion (27.1 ± 3.2 µg mL-1). The dissolution rate was significantly accelerated compared to pure lycopene. The molecular mechanism was further investigated by the integrated experimental and modeling tools. Molecular dynamics (MD) simulation revealed lycopene molecule was wrapped within the aggregates of hydroxypropyl-beta-cyclodextrin (HP-ß-CD) and PEG 6000 through extensive hydrogen bond interactions, which was experimentally validated by DSC, XRD, and FTIR spectrum analysis. The third component PEG 6000 facilitated the process of HME and augmented hydrogen bond interactions with HP-ß-CD. Moreover, lycopene inclusions exhibited significant antitumor activity via inhibiting cell proliferation and inducing apoptosis. The pharmacokinetic studies showed the relative bioavailability of lycopene ternary preparation was up to 313.08% and the Cmax was 4.9-fold higher than that of the marketed tablet. In conclusion, the lycopene cyclodextrin ternary formulation developed by the modified HME techniques is suitable for industrial production, while PEG 6000 plays a vital part in the multicomponent systems to increase solubility, dissolution rate, and oral bioavailability of lycopene. The combination of experimental and computational tools is able to benefit the development of multicomponent formulations accurately and effectively.

An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction.

BACKGROUND: Pharmacokinetic evaluation is one of the key processes in drug discovery and development. However, current absorption, distribution, metabolism, and excretion prediction models still have limited accuracy. AIM: This study aims to construct an integrated transfer learning and multitask learning approach for developing quantitative structure-activity relationship models to predict four human pharmacokinetic parameters. METHODS: A pharmacokinetic data set included 1104 U.S. FDA approved small molecule drugs. The data set included four human pharmacokinetic parameter subsets (oral bioavailability, plasma protein binding rate, apparent volume of distribution at steady-state, and elimination half-life). The pretrained model was trained on over 30 million bioactivity data entries. An integrated transfer learning and multitask learning approach was established to enhance the model generalization. RESULTS: The pharmacokinetic data set was split into three parts (60:20:20) for training, validation, and testing by the improved maximum dissimilarity algorithm with the representative initial set selection algorithm and the weighted distance function. The multitask learning techniques enhanced the model predictive ability. The integrated transfer learning and multitask learning model demonstrated the best accuracies, because deep neural networks have the general feature extraction ability; transfer learning and multitask learning improve the model generalization. CONCLUSIONS: The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and development.

Three-Level Hepatotoxicity Prediction System Based on Adverse Hepatic Effects.

Hepatotoxicity is a major cause of drug withdrawal from the market. To reduce the drug attrition induced by hepatotoxicity, an accurate and efficient hepatotoxicity prediction system must be constructed. In the present study, we constructed a three-level hepatotoxicity prediction system based on different levels of adverse hepatic effects (AHEs) combined with machine learning, using (1) an end point, hepatotoxicity; (2) four hepatotoxicity severity degrees; and (3) specific AHEs. After collecting and curing 15 873 compound-AHE pairs associated with 2017 compounds and 403 AHEs, we constructed 27 models with three end point levels with the random forest algorithm, and obtained accuracies ranging from 67.0 to 78.2% and the area under receiver operating characteristic curves (AUCs) of 0.715-0.875. The 27 models were fully integrated into a tiered hepatotoxicity prediction system. The existence of hepatotoxicity existence, severity degree, and potential AHEs for a given compound could be inferred simultaneously and systematically. Thus, the tiered hepatotoxicity prediction system allows researchers to have significant confidence in confirming compound hepatotoxicity, analyzing hepatotoxicity from multiple perspectives, obtaining warnings for the potential hepatotoxicity severity, and even rapidly selecting the proper in vitro experiments for hepatotoxicity verification. We also applied three external sets (11 drugs or candidates that failed in clinical trials or were withdrawn from the market, the PharmGKB (offsides) database, and an herbal hepatotoxicity data set) to test and validate the prediction ability of our system. Furthermore, the hepatotoxicity prediction system was adapted into a flow framework based on the Konstanz Information Miner, which was made available for researchers.

Resveratrol in experimental Alzheimer's disease models: A systematic review of preclinical studies.

Alzheimer's disease (AD) is a progressive dementia caused by degeneration of the central nervous system with a high incidence in the elderly. Resveratrol is a natural compound contained in a wide range of plant species, including grapes. Recent studies have shown positive effects of resveratrol in animal models of AD; however, whether these results justify clinical trials is uncertain. Furthermore, there are multiple theories about the mechanism(s) by which resveratrol works and knowing how it works can suggest targets for future drug development. So far, systematic evaluation of both of these aspects is lacking. In this study, we selected 19 studies describing the efficacy of resveratrol in rodent AD models by electronic and manual retrieval. The method quality of the study were analyzed by the SYRCLE's risk of bias tool and the experimental data were retrieved and meta-analyzed using forest plot. Analysis of these studies demonstrates the consistent neuroprotective effects of resveratrol in AD models and offers insights into the possible pharmacological mechanisms. This information eliminates the bias of each study, providing supporting evidence for the implementation of clinical trials. However, the limits of studies were also noticed: low method quality, lack of sample size calculation and high risks of bias.

A novel dual sensitive polymer-gambogic acid conjugate: synthesis, characterization, and in vitro evaluation.

Currently, bio-simulate drug delivery systems are highly considered for efficient targeting of tumors. Nevertheless, there are some potential problems such as intelligent release efficiency, subsequently, influence cell toxicity and blood circulation stability. A novel type of stimuli-responsive nanoparticle was developed in accordance with the specific tumor microenvironment to deliver gambogic acid (GA). Herein, we successfully connected GA with mPEG via two different sensitive linkages, valine-citrulline (VC) and cystamine. The structure was characterized by ESI-MS, 1H NMR, FT-IR or MALDI-TOF-MS. The mPEG-VC-SS-GA-NPs (PVSG-NPs) were rapidly prepared. The properties of nanoparticles, including solubility, particle size, morphology, and sensitive drug release performance, were investigated. Compared to single sensitive conjugate (mPEG-SS-GA-NPs, PSG-NPs), PVSG-NPs demonstrated greater solubility and higher sensitive release profile. Cytotoxicity test indicated that PVSG-NPs had apparent cytotoxicity on HepG2 cells and reduced cytotoxicity on normal cells. Additionally, PVSG-NPs mainly kill HepG2 cells by inducing early and late apoptosis and restraining the G0/G1 phase proliferation. Albumin adsorption test revealed that the PVSG-NPs had little albumin combination, consequently, enhancing their circulation constancy. In summary, our findings suggested the novel PVSG-NPs capable of being used for tumor targeting and further practical applications.

Insight into the Dissolution Molecular Mechanism of Ternary Solid Dispersions by Combined Experiments and Molecular Simulations.

With the increase concern of solubilization for insoluble drug, ternary solid dispersion (SD) formulations developed more rapidly than binary systems. However, rational formulation design of ternary systems and their dissolution molecular mechanism were still under development. Current research aimed to develop the effective ternary formulations and investigate their molecular mechanism by integrated experimental and modeling techniques. Glipizide (GLI) was selected as the model drug and PEG was used as the solubilizing polymer, while surfactants (e.g., SDS or Tween80) were the third components. SD samples were prepared at different weight ratio by melting method. In the dissolution tests, the solubilization effect of ternary system with very small amount of surfactant (drug/PEG/surfactant 1/1/0.02) was similar with that of binary systems with high polymer ratios (drug/PEG 1/3 and 1/9). The molecular structure of ternary systems was characterized by differential scanning calorimetry (DSC), infrared absorption spectroscopy (IR), X-ray diffraction (XRD), and scanning electron microscope (SEM). Moreover, molecular dynamic (MD) simulations mimicked the preparation process of SDs, and molecular motion in solvent revealed the dissolution mechanism of SD. As the Gordon-Taylor equation described, the experimental and calculated values of Tg were compared for ternary and binary systems, which confirmed good miscibility of GLI with other components. In summary, ternary SD systems could significantly decrease the usage of polymers than binary system. Molecular mechanism of dissolution for both binary and ternary solid dispersions was revealed by combined experiments and molecular modeling techniques. Our research provides a novel pathway for the further research of ternary solid dispersion formulations.

Computer-Aided Formulation Design for a Highly Soluble Lutein-Cyclodextrin Multiple-Component Delivery System.

Cyclodextrin (CD) complexation is widely used for the solubilization of poorly soluble drugs in the pharmaceutical industry. Current research was to develop a highly soluble lutein-cyclodextrin multiple-component delivery system (lutein-CD-MCDS) by combined modeling and experimental approaches. Both phase solubility diagram and molecular dynamics (MD) simulation results revealed that the interactions between lutein and CDs were very weak, which confirmed the insignificant solubility improvement of lutein-CD binary system. On the basis of theoretical calculation and preliminary CD studies, lutein-CD-MCDS was developed with over 400-fold solubility improvement after formulation screening. MD simulation indicated that the auxiliary polymers of TWEEN 80 and poloxamer 188 in the lutein-CD-MCDS introduced bridged interaction between lutein and γ-CD to increase the solubility, dissolution rate, and stability of the complex. The lutein-CD-MCDS was characterized by in vitro dissolution test, differential scanning colorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and powder X-ray diffraction (PXRD). Moreover, lutein-CD-MCDS had significantly higher uptake in Caco-2 cells than free lutein. The relative bioavailability of the lutein-CD-MCDS increased to 6.6-fold compared to pure lutein, and to 1.2-fold compared with commercial lutein soft capsules. In conclusion, the highly soluble lutein-CD-MCDS with significant improvement in both the solubility and bioavailability was developed and characterized by combined modeling and experimental approaches. Our research indicates that computer-aided formulation design is a promising approach for future formulation development.

Analysis of the Literature and Patents on Solid Dispersions from 1980 to 2015.

Background: Solid dispersions are an effective formulation technique to improve the solubility, dissolution rate, and bioavailability of water-insoluble drugs for oral delivery. In the last 15 years, increased attention was focused on this technology. There were 23 marketed drugs prepared by solid dispersion techniques. Objective: This study aimed to report the big picture of solid dispersion research from 1980 to 2015. Method: Scientific knowledge mapping tools were used for the qualitative and the quantitative analysis of patents and literature from the time and space dimensions. Results: Western Europe and North America were the major research areas in this field with frequent international cooperation. Moreover, there was a close collaboration between universities and industries, while research collaboration in Asia mainly existed between universities. The model drugs, main excipients, preparation technologies, characterization approaches and the mechanism involved in the formulation of solid dispersions were analyzed via the keyword burst and co-citation cluster techniques. Integrated experimental, theoretical and computational tools were useful techniques for in silico formulation design of the solid dispersions. Conclusions: Our research provided the qualitative and the quantitative analysis of patents and literature of solid dispersions in the last three decades.

Release Mechanism Between Ion Osmotic Pressure and Drug Release in Ionic-Driven Osmotic Pump Tablets (I).

The objective of this study was to develop an authentic ionic-driven osmotic pump system and investigate the release mechanism, simultaneously exploring the in vitro and in vivo correlation of the ionic-driven osmotic pump tablet. A comparison of the ionic-driven and conventional theophylline osmotic pump, the influence of pH and the amount of sodium chloride on drug release, the relationship between the ionic osmotic pressure and the drug release, and the pharmacokinetics experiment in beagle dogs were investigated. Consequently, the similarity factor (f 2 ) between the novel and conventional theophylline osmotic pump tablet was 60.18, which indicated a similar drug-release behavior. Also, the release profile fitted a zero-order kinetic model. The relative bioavailability of the ionic-driven osmotic pump to the conventional osmotic pump calculated from the AUC (0-∞) was 93.6% and the coefficient (R = 0.9945) confirmed that the ionic-driven osmotic pump exhibited excellent IVIVC. The driving power of the ionic-driven osmotic pump was produced only by ions, which was strongly dependent on the ion strength, and a novel formula for the ionic-driven osmotic pump was derived which indicated that the drug-release rate was proportional to the ionic osmotic pressure and the sodium chloride concentration. Significantly, the formula can predict the drug-release rate and release characteristics of theophylline ionic-driven osmotic pumps, guiding future modification of the ionic osmotic pump.

Novel Hsp90 inhibitor platycodin D disrupts Hsp90/Cdc37 complex and enhances the anticancer effect of mTOR inhibitor.

Heat shock protein 90 (Hsp90) is a critically conserved molecular chaperone protein and promising therapeutic target for cancer treatment. In this study, platycodin D (PD), a saponin isolated from traditional Chinese herb Platycodonis Radix, was identified as a novel Hsp90 inhibitor. We verified that PD did not affect the ATPase activity of Hsp90. However, PD disrupted the co-chaperone interaction of Hsp90/cell division cycle protein 37 (Cdc37) and subsequently degraded multiple Hsp90 client proteins without the feedback increase of Hsp70. In different genotypes of non-small cell lung cancer cells, co-treatment with the mTOR inhibitor Everolimus and PD enhanced antiproliferation activity and apoptotic effect. The feedback survival signal upon mTOR inhibition was fully terminated by the co-administration with PD through reduced epidermal growth factor receptor (EGFR) and insulin growth factor 1 receptor (IGF1R) expression, suppressed AKT activity, and reinforced 4E-BP1 inhibition. Our results not only identified PD as a novel Hsp90 inhibitor by disrupting the protein-protein interaction of Hsp90/Cdc37 complex, but also provided mechanistic insights into the ineffectiveness of mTOR inhibitors and identified therapeutic strategy for cancer treatment.