Qualification of a non-animal vaginal irritation method admitted as nonclinical assessment model (NAM) in the Incubator Phase of the United States Food and Drug Administration (US FDA) Medical Devices Development Tool (MDDT)

International Comparison of Qualification Process for Medical Product Development Tools

INTRODUCTION

Qualification of medical product evaluation tools is underway in the United States, Europe, and Japan to reflect the advancements in the basic science of medical products. In Europe and the U.S., Guidance of Guidances (GoG) policies that clarify regulators'processes, tasks, and methods of sponsor involvement are adopted to issue tool guidance. However, in Japan, a non-GoG type policy focusing on supporting the research and development for tools without defining a tool guidance-making process has been adopted.

METHODS

In this study, an analytical framework for the lifecycle of development tools was constructed, including pre- and post-tool qualification processes, to compare the two above-mentioned approaches. For this study, Japanese cases were selected as experimental cases, whereas Western cases served as controls. The progress of tool qualification and composition of deliverables were analyzed.

RESULTS AND CONCLUSIONS

It was indicated that in the GoG type policy, in which processes are defined, and involvement methods are clarified, tool qualification can progress more smoothly than in a non-GoG type policy. This policy indicates that deliverables may have a consistent composition. Contrastingly, GoG-type policies alone present challenges in connecting upstream tools for R&D support.

Vaginal irritation testing-prospects of human organotypic vaginal tissue culture models

Personal lubricants intended for local or systemic delivery via the vaginal route can induce vaginal irritation, damage the vaginal epithelial barrier which can enhance microbial entry, induce inflammation, and alter the microbiome of the vaginal ecosystem. Therefore, manufacturers of personal lubricants and medical devices are required to show biocompatibility and safety assessment data to support regulatory decision-making within a specified context of use. Furthermore, due to ethical concerns and the introduction of the 7th amendment of the European Council Directive which bans animal testing for cosmetic ingredients and products coupled with the Food and Drug Administration modernization Act 2.0 guidelines, there is a wave of drive to develop alternative test methods to predict human responses to chemical or formulation exposure. In this framework, there is a potential to use three-dimensional organotypic human vaginal-ectocervical tissue models as a screening tool to predict the vaginal irritation potential of personal lubricants and medicaments. To be physiologically relevant, the in vitro tissue models need to be reconstructed using primary epithelial cells of the specific organ or tissue and produce organ-like structure and functionality that recapitulate the in vivo-like responses. Through the years, progress has been made and vaginal tissue models are manufactured under controlled conditions with a specified performance criterion, which leads to a high level of reproducibility and reliability. The utility of vaginal tissue models has been accelerated in the last 20 years with an expanded portfolio of applications ranging from toxicity, inflammation, infection to drug safety, and efficacy studies. This article provides an overview of the state of the art of diversified applications of reconstructed vaginal tissue models and highlights their utility as a tool to predict vaginal irritation potential of feminine care products.

Updated in Silico Prediction Methods for Fractions Absorbed and Key Input Parameters of 355 Disparate Chemicals for Physiologically Based Pharmacokinetic Models for Time-Dependent Plasma Concentrations after Virtual Oral Doses in Humans

Human metabolic profiles for substances such as toxic food-derived compounds are usually allometrically extrapolated from traditionally determined in vivo rat concentration profiles. To evaluate internal exposures in humans without any reference to experimental data, physiologically based pharmacokinetic (PBPK) modeling could be used if the model input parameters could be estimated in silico. This approach would simplify the use of PBPK models for forward dosimetry after oral doses. In this study, the in silico estimation of input parameters for PBPK models (i.e., fraction absorbed × intestinal availability, absorption rate constants, and volumes of the systemic circulation) was updated for an panel of 355 chemicals (212 previously analyzed and 143 additional substances) using a light gradient boosting machine learning algorithms (LightGBM) based on between 11 and 29 in silico-calculated chemical descriptors. Simplified human PBPK models were then used to calculate virtual maximum plasma concentrations (C_max) and areas under the concentration-time curve (AUC) based on two sets of input parameters, i.e., traditionally derived values from in vivo data and those calculated in silico using the current updated systems. Both sets of C_max and AUC data were well correlated (r = 0.87 and r = 0.73, respectively; p < 0.01, n = 355). Therefore, input parameters for human PBPK models for a diverse range of compounds could be successfully estimated using chemical descriptors and in silico tools. This approach to pharmacokinetic modeling has potential for application in computational toxicology and in the clinical setting for assessing the potential risk of general chemicals.

Machine Learning Prediction of the Three Main Input Parameters of a Simplified Physiologically Based Pharmacokinetic Model Subsequently Used to Generate Time-Dependent Plasma Concentration Data in Humans after Oral Doses of 212 Disparate Chemicals

Physiologically based pharmacokinetic (PBPK) modeling has the potential to play significant roles in estimating internal chemical exposures. The three major PBPK model input parameters (i.e., absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearances) were generated in silico for 212 chemicals using machine learning algorithms. These input parameters were calculated based on sets of between 17 and 65 chemical properties that were generated by in silico prediction tools before being processed by machine learning algorithms. The resulting simplified PBPK models were used to estimate plasma concentrations after virtual oral administrations in humans. The estimated absorption rate constants, volumes of the systemic circulation, and hepatic intrinsic clearance values for the 212 test compounds determined traditionally (i.e., based on fitting to measured concentration profiles) and newly estimated had correlation coefficients of 0.65, 0.68, and 0.77 (p < 0.01, n = 212), respectively. When human plasma concentrations were modeled using traditionally determined input parameters and again using in silico estimated input parameters, the two sets of maximum plasma concentrations (r = 0.85, p < 0.01, n = 212) and areas under the curve (r = 0.80, p < 0.01, n = 212) were correlated. Virtual chemical exposure levels in liver and kidney were also estimated using these simplified PBPK models along with human plasma levels. These results indicate that the PBPK model input parameters for humans of a diverse set of compounds can be reliability estimated using chemical descriptors calculated using in silico tools.

An Updated In Silico Prediction Method for Volumes of Systemic Circulation of 323 Disparate Chemicals for Use in Physiologically Based Pharmacokinetic Models to Estimate Plasma and Tissue Concentrations after Oral Doses in Rats

Updated algorithms for predicting the volumes of systemic circulation (V₁), along with absorption rate constants and hepatic intrinsic clearances, as input parameters for physiologically based pharmacokinetic (PBPK) models were established to improve the accuracy of estimated plasma and tissue concentrations of 323 chemicals after virtual oral administrations in rats. Using ridge regression with an enlarged set of chemical descriptors (up to 99), the estimated input V₁ values resulted in an improved correlation coefficient (from 246 compounds) with the traditionally determined values. The PBPK model input parameters for rats of diverse compounds can be precisely estimated by increasing the number of descriptors.

In silico Prediction of Skin Sensitization: Quo vadis?

Skin direct contact with chemical or physical substances is predisposed to allergic contact dermatitis (ACD), producing various allergic reactions, namely rash, blister, or itchy, in the contacted skin area. ACD can be triggered by various extremely complicated adverse outcome pathways (AOPs) remains to be causal for biosafety warrant. As such, commercial products such as ointments or cosmetics can fulfill the topically safe requirements in animal and non-animal models including allergy. Europe, nevertheless, has banned animal tests for the safety evaluations of cosmetic ingredients since 2013, followed by other countries. A variety of non-animal in vitro tests addressing different key events of the AOP, the direct peptide reactivity assay (DPRA), KeratinoSens™, LuSens and human cell line activation test h-CLAT and U-SENS™ have been developed and were adopted in OECD test guideline to identify the skin sensitizers. Other methods, such as the SENS-IS are not yet fully validated and regulatorily accepted. A broad spectrum of in silico models, alternatively, to predict skin sensitization have emerged based on various animal and non-animal data using assorted modeling schemes. In this article, we extensively summarize a number of skin sensitization predictive models that can be used in the biopharmaceutics and cosmeceuticals industries as well as their future perspectives, and the underlined challenges are also discussed.

In Silico Prediction of Input Parameters for Simplified Physiologically Based Pharmacokinetic Models for Estimating Plasma, Liver, and Kidney Exposures in Rats after Oral Doses of 246 Disparate Chemicals

Recently developed computational models can estimate plasma, hepatic, and renal concentrations of industrial chemicals in rats. Typically, the input parameter values (i.e., the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance) for simplified physiologically based pharmacokinetic (PBPK) model systems are calculated to give the best fit to measured or reported in vivo blood substance concentration values in animals. The purpose of the present study was to estimate in silico these three input pharmacokinetic parameters using a machine learning algorithm applied to a broad range of chemical properties obtained from several cheminformatics software tools. These in silico estimated parameters were then incorporated into PBPK models for predicting internal exposures in rats. Following this approach, simplified PBPK models were set up for 246 drugs, food components, and industrial chemicals with a broad range of chemical structures. We had previously generated PBPK models for 158 of these substances, whereas 88 for which concentration series data were available in the literature were newly modeled. The values for the absorption rate constant, volume of systemic circulation, and hepatic intrinsic clearance could be generated in silico by equations containing between 14 and 26 physicochemical properties. After virtual oral dosing, the output concentration values of the 246 compounds in plasma, liver, and kidney from rat PBPK models using traditionally determined and in silico estimated input parameters were well correlated (r ≥ 0.83). In summary, by using PBPK models consisting of chemical receptor (gut), metabolizing (liver), excreting (kidney), and central (main) compartments with in silico-derived input parameters, the forward dosimetry of new chemicals could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of hematotoxic, hepatotoxic, or nephrotoxic potential as a part of risk assessment.