Protocol development, validation, and troubleshooting of in-situ fiber optic bathless dissolution system (FODS) for a pharmaceutical drug testing

Bayesian statistical approaches to drug product variability assessment and release

The determination of the variability of critical dosage form attributes has been a challenge in establishing the quality of pharmaceutical products. During the development process knowledge is minimal. Consequently, ad hoc statistical tools such as hypothesis or significance tests, with calibrated decision error rates are often used in an effort to vet CQAs (Critical Quality Attributes) and keep their levels "between the curbs". As progress moves towards product launch, process and mechanistic understanding grows considerably and there are opportunities to leverage that knowledge for predictive modeling. Bayesian models offer a coherent strategy for integrating prior knowledge into both experimental design as well as predictive analysis for optimal risk-based decision making. This is because the Bayesian paradigm, unlike the frequentist paradigm, can assign probabilities to underlying states of nature that directly impact safety and efficacy such as the population distribution of tablet potencies or dissolution profiles in a batch. However, there are challenges and reluctance in switching to a predictive modeling quality framework once regulatory approval has been attained. This paper offers encouragement to make this switch. In this paper, we review a joint Long Island University - Purdue University (LIU-PU) FDA funded project whose purpose was to further integrate the concepts of this adaptive approach to lot release with the rationale and methods for data generation and curation and to extend the testing of this approach. We discuss the utility of the approach in product development. We consider the regulatory compliance implications, with examples, and establish a potential way forward toward implementation of this approach for both industry and regulatory stake-holders.

Accelerating pre-formulation investigations in early drug product life cycles using predictive methodologies and computational algorithms

Precisely developed computational methodologies can allow the drug product lifecycle process to be time-efficient, cost-effective and reliable through a thorough fundamental understanding at the molecular level. Computational methodologies include computational simulations, virtual screening, mathematical modeling and predictive tools. In light of current trends and increased expectations of product discovery in early pharmaceutical development, we have discussed different case studies. These case studies clearly demonstrate the successful application of predictive tools alone or in combination with analytical techniques to predict the physicochemical properties of drug substances and drug products, thereby shortening research and development timelines. The overall goal of this report is to summarize unique predictive methodologies, which can assist pharmaceutical scientists in achieving time-sensitive research goals and avoiding associated risks that can potentially affect the drug product quality.

Understanding the Impact of Multi-factorial Composition on Efficient Loading of the Stable Ketoprofen Nanoparticles on Orodispersible Films Using Box-Behnken Design

The purpose of the present study was to prepare Orodispersible films (ODFs) loaded with ketoprofen nanoparticles (KT-NP). The Box-Behnken design was constructed in developing and optimizing the KTF-NP-ODFs. The effect of independent variables: Soluplus® concentration (X₁, stabilizer), Tween 80 concentration (X₂, surfactant), and KTF concentration (X₃, drug) were studied on the dependent variables: particle size (PS, Y₁), zeta potential (ZP, Y₂), and the polydispersity index (PDI, Y₃) of the NPs, as well as on the tensile strength (TS, Y₄) and permeability coefficient (PC, Y₅) of the KTF-NP-ODFs. Hydroxypropyl methylcellulose (HPMC E15) and polyethylene glycol (PEG 400) were used as the film former polymer and plasticizer, respectively, and their concentrations were kept constant for all formulations. KTF-NPs were prepared by antisolvent precipitation technology. This was followed by the addition of HPMC E15 and PEG 400 to prepare the ODFs using the solvent-casting method. The PS, PDI, and ZP for all the formulations were found in the range of 94 nm to 350 nm, 0.09 to 0.438, and -21.83 mV to -8.03 mV, respectively. The TS and PC of the prepared KTF-NP-ODFs were found between 1.21 MPa to 3.93 MPa and 3.12 × 10^-4 cm/h to 34.23 × 10^-4 cm/h, respectively. The amorphous nature of the KTF-NP in the ODFs was confirmed by the absence of characteristic crystalline peaks and endothermic events of KTF in X-ray diffraction (XRD) and modulated differential scanning calorimetry (mDSC), respectively. The optimized formulation showed ̴ 4 times higher permeability as compared to the pure KTF. In addition, the dissolution of pure KTF and the optimized KTF-NP-ODF in pH 1.2 at the end of 60 min was found to be ̴ 30% and ̴ 95%, respectively. Conclusively, KTF-NP-ODFs can be a promising drug delivery system to counter the issues related to dysphagia and bypass the common side effects, such as the gastric irritation associated with NSAIDs like KTF.