Development of an In Vitro Drug Release Method to Enable In Vitro-In Vivo Correlation of Potassium Chloride Extended-Release Tablets.

An in vitro drug release test was developed to establish a level A in vitro-in vivo correlation (IVIVC) for predicting the in vivo performance of potassium chloride extended-release (ER) matrix tablets. Three ER formulations of potassium chloride with different in vitro release rates were designed using the USP dissolution test, and their urinary pharmacokinetic profiles were evaluated in healthy subjects. Due to the lack of IVIVC with the USP method, experiments were designed to investigate the effects of in vitro test conditions on drug release in order to match in vitro drug release with in vivo behaviors of different formulations. The evaluated in vitro variables included the type of USP apparatus, surfactant, and ionic strength of the dissolution medium. Based on the study findings and data analysis, a discriminatory drug release method was successfully developed that enabled the establishment and validation of a level A IVIVC model of the potassium chloride ER tablet using urinary pharmacokinetic data. This method uses USP apparatus I at 50 rpm in 900 mL of 150 mM NaCl solution containing 40 mM sodium dodecyl sulfate at 37 °C. The current study highlights the value of investigating test conditions in developing a predictive in vitro test method for establishing IVIVC.

Melting point prediction of organic molecules by deciphering the chemical structure into a natural language.

Establishing quantitative structure-property relationships for the rational design of small molecule drugs at the early discovery stage is highly desirable. Using natural language processing (NLP), we proposed a machine learning model to process the line notation of small organic molecules, allowing the prediction of their melting points. The model prediction accuracy benefits from training upon different canonicalized SMILES forms of the same molecules and does not decrease with increasing size, complexity, and structural flexibility. When a combination of two different canonicalized SMILES forms is used to train the model, the prediction accuracy improves. Largely distinguished from the previous fragment-based or descriptor-based models, the prediction accuracy of this NLP-based model does not decrease with increasing size, complexity, and structural flexibility of molecules. By representing the chemical structure as a natural language, this NLP-based model offers a potential tool for quantitative structure-property prediction for drug discovery and development.

Modeling Physical Stability of Amorphous Solids Based on Temperature and Moisture Stresses.

Isothermal microcalorimetry was utilized to monitor the crystallization process of amorphous ritonavir (RTV) and its hydroxypropylmethylcellulose acetate succinate-based amorphous solid dispersion under various stressed conditions. An empirical model was developed: ln(τ)=ln(A)+EaRT-b⋅wc, where τ is the crystallization induction period, A is a pre-exponential factor, Ea is the apparent activation energy, b is the moisture sensitivity parameter, and wc is water content. To minimize the propagation of errors associated with the estimates, a nonlinear approach was used to calculate mean estimates and confidence intervals. The physical stability of neat amorphous RTV and RTV in hydroxypropylmethylcellulose acetate succinate solid dispersions was found to be mainly governed by the nucleation kinetic process. The impact of polymers and moisture on the crystallization process can be quantitatively described by Ea and b in this Arrhenius-type model. The good agreement between the measured values under some less stressful test conditions and those predicted, reflected by the slope and R(2) of the correlation plot of these 2 sets of data on a natural logarithm scale, indicates its predictability of long-term physical stability of amorphous RTV in solid dispersions. To further improve the model, more understanding of the impact of temperature and moisture on the amorphous physical stability and fundamentals regarding nucleation and crystallization is needed.

A novel accelerated oxidative stability screening method for pharmaceutical solids.

Despite the fact that oxidation is the second most frequent degradation pathway for pharmaceuticals, means of evaluating the oxidative stability of pharmaceutical solids, especially effective stress testing, are still lacking. This paper describes a novel experimental method for peroxide-mediated oxidative stress testing on pharmaceutical solids. The method utilizes urea-hydrogen peroxide, a molecular complex that undergoes solid-state decomposition and releases hydrogen peroxide vapor at elevated temperatures (e.g., 30°C), as a source of peroxide. The experimental setting for this method is simple, convenient, and can be operated routinely in most laboratories. The fundamental parameter of the system, that is, hydrogen peroxide vapor pressure, was determined using a modified spectrophotometric method. The feasibility and utility of the proposed method in solid form selection have been demonstrated using various solid forms of ephedrine. No degradation was detected for ephedrine hydrochloride after exposure to the hydrogen peroxide vapor for 2 weeks, whereas both anhydrate and hemihydrate free base forms degraded rapidly under the test conditions. In addition, both the anhydrate and the hemihydrate free base degraded faster when exposed to hydrogen peroxide vapor at 30°C under dry condition than at 30°C/75% relative humidity (RH). A new degradation product was also observed under the drier condition. The proposed method provides more relevant screening conditions for solid dosage forms, and is useful in selecting optimal solid form(s), determining potential degradation products, and formulation screening during development.