Refine
Year of publication
- 2020 (104) (remove)
Document Type
- Article (49)
- Doctoral Thesis (46)
- Preprint (7)
- Report (2)
Has Fulltext
- yes (104) (remove)
Is part of the Bibliography
- no (104) (remove)
Keywords
- DNA-PAINT (3)
- Non-structural protein (3)
- PBPK (3)
- SARS-CoV-2 (3)
- Solution NMR-spectroscopy (3)
- fluorescence (3)
- Biochemistry (2)
- Covid19-NMR (2)
- Medicinal chemistry (2)
- NMR (2)
Institute
- Biochemie, Chemie und Pharmazie (104) (remove)
Mesoporous silica has emerged as an enabling formulation for poorly soluble active pharmaceutical ingredients (APIs). Unlike other formulations, mesoporous silica typically does not inhibit precipitation of supersaturated API therefore, a suitable precipitation inhibitor (PI) should be added to increase absorption from the gastrointestinal (GI) tract. However, there is limited research about optimal processes for combining PIs with silica formulations. Typically, the PI is added by simply blending the API-loaded silica mechanically with the selected PI. This has the drawback of an additional blending step and may also not be optimal with regard to release of drug and PI. By contrast, loading PI simultaneously with the API onto mesoporous silica, i.e. co-incorporation, is attractive from both a performance and practical perspective. The aim of this study was to demonstrate the utility of a co-incorporation approach for combining PIs with silica formulations, and to develop a mechanistic rationale for improvement of the performance of silica formulations using the co-incorporation approach. The results indicate that co-incorporating HPMCAS with glibenclamide onto silica significantly improved the extent and duration of drug supersaturation in single-medium and transfer dissolution experiments. Extensive spectroscopic characterization of the formulation revealed that the improved performance was related to the formation of drug-polymer interactions already in the solid state; the immobilization of API-loaded silica on HPMCAS plates, which prevents premature release and precipitation of API; and drug-polymer proximity on disintegration of the formulation, allowing for rapid onset of precipitation inhibition. The data suggests that co-incorporating the PI with the API is appealing for silica formulations from both a practical and formulation performance perspective.
Background: Physiologically-based population pharmacokinetic modeling (popPBPK) coupled with in vitro biopharmaceutics tools such as biorelevant dissolution testing can serve as a powerful tool to establish virtual bioequivalence and set clinically relevant specifications. One of several applications of popPBPK modeling is in the emerging field of virtual bioequivalence (VBE), where it can be used to streamline drug development by implementing model-informed formulation design and to inform regulatory decision-making e.g., with respect to evaluating the possibility of extending BCS-based biowaivers beyond BCS Class I and III compounds in certain cases.
Methods: In this study, Naproxen, a BCS class II weak acid was chosen as the model compound. In vitro biorelevant solubility and dissolution experiments were performed and the resulting data were used as an input to the PBPK model, following a stepwise workflow for the confirmation of the biopharmaceutical parameters. The naproxen PBPK model was developed by implementing a middle-out approach and verified against clinical data obtained from the literature. Once confidence in the performance of the model was achieved, several in vivo dissolution scenarios, based on model-based analysis of the in vitro data, were used to simulate clinical trials in healthy adults. Inter-occasion variability (IOV) was also added to critical physiological parameters and mechanistically propagated through the simulations. The various trials were simulated on a “worst/best case” dissolution scenario and average bioequivalence was assessed according to Cmax, AUC and tmax.
Results: VBE results demonstrated that naproxen products with in vitro dissolution reaching 85% dissolved within 90 minutes would lie comfortably within the bioequivalence limits for Cmax and AUC. Based on the establishment of VBE, a dissolution “safe space” was designed and a clinically relevant specification for naproxen products was proposed. The interplay between formulation-related and drug-specific PK parameters (e.g., t1/2) to predict the in vivo performance was also investigated.
Conclusion: Over a wide range of values, the in vitro dissolution rate is not critical for the clinical performance of naproxen products and therefore naproxen could be eligible for BCS-based biowaivers based on in vitro dissolution under intestinal conditions. This approach may also be applicable to other poorly soluble acidic compounds with long half-lives, providing an opportunity to streamline drug development and regulatory decision-making without putting the patient at a risk.
In the context of data science, data projection and clustering are common procedures. The chosen analysis method is crucial to avoid faulty pattern recognition. It is therefore necessary to know the properties and especially the limitations of projection and clustering algorithms. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). The FCPS contains 10 datasets with the names "Atom", "Chainlink", "EngyTime", "Golfball", "Hepta", "Lsun", "Target", "Tetra", "TwoDiamonds", and "WingNut". Common clustering methods occasionally identified non-existent clusters or assigned data points to the wrong clusters in the FCPS suite. Likewise, common data projection methods could only partially reproduce the data structure correctly on a two-dimensional plane. In conclusion, the FCPS dataset collection addresses general challenges for clustering and projection algorithms such as lack of linear separability, different or small inner class spacing, classes defined by data density rather than data spacing, no cluster structure at all, outliers, or classes that are in contact. This report describes a collection of datasets that are grouped together in the Fundamental Clustering and Projection Suite (FCPS). It is designed to address specific problems of structure discovery in high-dimensional spaces.
Introduction: In the development of bio-enabling formulations, innovative in vivo predictive tools to understand and predict the in vivo performance of such formulations are needed. Etravirine, a non-nucleoside reverse transcriptase inhibitor, is currently marketed as an amorphous solid dispersion (Intelence® tablets). The aims of this study were 1) to investigate and discuss the advantages of using biorelevant in vitro setups in simulating the in vivo performance of Intelence® 100 mg and 200 mg tablets, in the fed state, 2) to build a Physiologically Based Pharmacokinetic (PBPK) model by combining experimental data and literature information with the commercially available in silico software Simcyp® Simulator V17.1 (Certara UK Ltd.), and 3) to discuss the challenges when predicting the in vivo performance of an amorphous solid dispersion and identify the parameters which influence the pharmacokinetics of etravirine most.
Methods: Solubility, dissolution and transfer experiments were performed in various biorelevant media simulating the fasted and fed state environment in the gastrointestinal tract. An in silico PBPK model for healthy volunteers was developed in the Simcyp® Simulator, using in vitro results and data available from the literature as input. The impact of pre- and post-absorptive parameters on the pharmacokinetics of etravirine was investigated using simulations of various scenarios.
Results: In vitro experiments indicated a large effect of naturally occurring solubilizing agents on the solubility of etravirine. Interestingly, supersaturated concentrations of etravirine were observed over the entire duration of dissolution experiments on Intelence® tablets. Coupling the in vitro results with the PBPK model provided the opportunity to investigate two possible absorption scenarios, i.e. with or without implementation of precipitation. The results from the simulations suggested that a scenario in which etravirine does not precipitate is more representative of the in vivo data. On the post-absorptive side, it appears that the concentration dependency of the unbound fraction of etravirine in plasma has a significant effect on etravirine pharmacokinetics.
Conclusions: The present study underlines the importance of combining in vitro and in silico biopharmaceutical tools to advance our knowledge in the field of bio-enabling formulations. Future studies on other bio-enabling formulations can be used to further explore this approach to support rational formulation design as well as robust prediction of clinical outcomes.