Permeation enhancers (PEs) are a class of amphiphilic molecules that promote the passive permeation of drug molecules through cellular membranes. There is currently a lack of understanding of the mechanism of action for permeation enhancers. Two PEs are studied in this manuscript, sodium caprate (C10) and salcaprozate sodium (SNAC), which are commonly used in coformulations and clinical trials. All-atom molecular dynamics simulations are carried out to probe their interactions with a membrane made of POPC. The permeation of water and three charged small molecule drugs (ketoprofen, propranolol, and salicylic acid) through a PE-embedded POPC membrane is studied. The results show that 1) the lipid bilayers can be saturated with PEs up to 250 mol % without losing structural integrity, 2) PEs embedded in the membrane facilitate the insertion of water into the tail groups in the center of the membrane, and 3) charged molecules, which are known to have a prohibitively high free energy barrier of permeation, have a significantly decreased free energy barrier of permeation in the presence of high concentration of PEs. Hydrogen bond contacts made between PEs and water molecules with the charged molecules when they are in the center of the membrane are primarily responsible for the decrease in the free energy barrier. This study provides insight about the permeation of larger molecules, which may have multiple charged states, namely peptides.

Passive permeation through an epithelial membrane may be enhanced by using a class of amphiphilic molecules known as permeation enhancers (PEs). PEs have been studied in clinical trials and used in coformulations with peptides and small molecule drugs, and yet, an understanding of the permeant-PE interactions leaves much to be desired. This manuscript uses all-atom molecular dynamics (MD) simulations to showcase the effects of sodium caprate (C10) and salcaprozate sodium (SNAC), two commonly applied PEs, on membrane properties and the free energy profiles of five small molecule drugs (mannitol, atenolol, ketoprofen, decanedecaol, mucic acid). Our results show that both C10 and SNAC make the lipid molecules pack more densely, but C10 increases the lipid lateral diffusivity while SNAC decreases it. The change in the lipid order parameter also shows both PEs increasing the order near the lipid heads, possibly due to the dense packing in the membrane. A decrease in the central barrier of the permeation free energy was observed by embedding PEs into a lipid bilayer and SNAC is more efficient in doing so than C10. Neither SNAC nor C10 has a large impact on the diffusion coefficient of the small molecules. The analysis of the MD simulations revealed that PEs make the membrane tail region more hydrophilic by forming hydrogen bonds with small molecule drugs, i.e., decreasing the central barrier of the permeation free energy. While this study was only limited to small molecule drugs, this lays the groundwork for future studies to which the effects of the PEs in the permeation of macromolecules and peptides may be observed.

Pharmaceutical innovators and generic companies use Physiologically Based Biopharmaceutics Models (PBBMs) to guide drug product development and potentially waive clinical pharmacokinetic studies for both pre- and postapproval changes. This modeling approach can assist with biopharmaceutics risk assessment and the establishment of patient centric, clinically relevant drug product specifications. However, the variability of possible model strategies and the existence of gaps in scientific knowledge associated with the lack of standardized regulatory expectations for model parametrization, data requirements for model development, and criteria for fit-for-purpose model validation leads to varied acceptance rates and frequent requests for additional information and deficiencies in PBBM submissions across regulatory agencies. During the 2023 Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI) PBBM Best Practices for Drug Product Quality: Regulatory and Industry Perspectives workshop, it was identified that a PBBM report template summarizing model considerations and proposing a structure for presenting question(s) of interest, model context, input data, a modeling plan, and validation would be beneficial for both industry and regulatory agencies. The present work is not a regulatory guideline but rather a summary of current best practices and considerations for PBBM submissions. The associated template can be downloaded directly from the Supporting Information to guide one in the preparation of PBBM reports. The current paper discusses the critical elements of the PBBM report template, which were identified during the industry-regulator scientific collaboration and interactions.

Dulaglutide, a long-acting glucagon-like peptide-1 (GLP-1) receptor agonist, is approved for improving glycemic control and reducing cardiovascular risks in patients with type 2 diabetes mellitus (T2DM). This research investigates the effect of dulaglutide on gastric emptying and its impact on the pharmacokinetics (PK) of orally administered molecules utilizing a combination of population pharmacokinetic (PopPK) and physiologically based pharmacokinetic (PBPK) modeling approaches. In clinical studies, the gastric emptying delay (GED) was evaluated in healthy participants and patients with T2DM at various dose levels of dulaglutide. A PopPK model estimated the exposure-dependent delay in gastric emptying, which was then input into the orally administered small molecule PBPK models. These PBPK models, informed by internal clinical studies and publicly available data, quantified the effect of dulaglutide-induced GED on the area under the curve (AUC), maximum concentration (Cmax), and time to maximum concentration (tmax) of the co-administered drugs. The modeling approach was verified for reproducing observed GED-mediated drug-drug interactions (DDIs) at low doses of dulaglutide and to predict DDIs at a 4.5 mg dulaglutide dose. The clinical studies demonstrated that the 1.5 mg dulaglutide dose has no clinically relevant effect on the pharmacokinetics of small molecules, and the modeling led to a similar conclusion at 4.5 mg dulaglutide. This work demonstrates that modeling approaches can be used to predict potential GLP-1-mediated DDIs related to gastric emptying delay, increasing the efficiency of the clinical pharmacology programs.

Permeation enhancers (PEs) are a class of molecules that interact with the epithelial membrane and transiently increase its transcellular permeability. Although there have been few clinical trials of PE coformulated drugs, the mechanism of action of PEs remains elusive. In this paper, the interaction between two archetypes of PEs [salcaprozate sodium (SNAC) and sodium caprate (C10)] and membranes is investigated with extensive all-atom molecular dynamics simulations. The simulations show that (1) the association between the neutral PEs and membranes is favored in free energy, (2) the propensity of neutral PE aggregation is larger in aqueous solution than in lipid bilayers, (3) the equilibrium distribution of neutral PEs in membranes is fast, e.g., accessible with unbiased MD simulations, and (4) the micelle of neutral PEs formed in aqueous solution does not rupture the membranes (e.g., not forming pores or breaking up the membrane) under simulation conditions. All results combined, this study indicates that PEs insert into the membranes in an equilibrium or near equilibrium process. This study lays the foundation for future investigations of how PEs impact the free energy of permeation for small molecules.

Design space definition is one of the key issues in pharmaceutical research and development. Flexibility index and design centering are two complementary ways to estimate a candidate design space. In this study, we first propose a novel formulation of flexibility index based on a direction search method, which is applied to any shape of the design space. Next, we propose two design centering methods. The vertex direction search method is first developed as a single-level optimization model, which is rigorous for convex regions. Next, based on the proposed flexibility index model, a derivative-free optimization (DFO) method is developed for solving the bi-level optimization models involved in design centering problems, which is applicable not only to convex but also to nonconvex problems. In order to find near global solutions, latin hypercube sampling (LHS) is used to generate multiple starting points for the DFO solver. The solution yields the optimal nominal point, which is the candidate point with the largest flexibility index. Several case studies demonstrate the performance of the proposed methods.

Design space definition is one of the key parts in pharmaceutical research and development. In this article, we propose a novel solution strategy to explicitly describe the design space without recourse decisions. First, to smooth the boundary, the Kreisselmeier–Steinhauser (KS) function is applied to aggregate all inequality constraints. Next, for creating a surrogate polynomial model of the KS function, we focus on finding sampling points on the boundary of KS space. After performing Latin hypercube sampling (LHS), two methods are presented to efficiently expand the boundary points, that is, line projection to the boundary through any two feasible LHS points and perturbation around the adaptive sampling points. Finally, a symbolic computation method, cylindrical algebraic decomposition, is applied to transform the surrogate model into a series of explicit and triangular subsystems, which can be converted to describe the KS space. Two case studies show the efficiency of the proposed algorithm.

The existing methods of flexibility index are mainly based on mixed-integer linear or nonlinear programming methods, making it difficult to readily deal with complex mathematical models. In this article, a novel solution strategy is proposed for finding a reliable upper bound of the flexibility index where the process model is implemented in a black box that can be directly executed by a commercial simulator, and also avoiding the need for calculating derivatives. Then, the flexibility index problem is formulated as a sequence of univariate derivative-free optimization (DFO) models. An external DFO solver based on trust-region methods can be called to solve this model. Finally, after calculating the critical point of the model parameters, the vertex enumeration method and two gradient approximation methods are proposed to evaluate the impact of process parameters and to evaluate the flexibility index. A reaction model is studied to show the efficiency of the proposed algorithm.

Oral drug absorption modeling has developed at a rapid pace in the 40 years or so since the first ideas for mathematical approaches to oral absorption were introduced. The success of compartmental approaches accelerated the uptake of absorption modeling, and over the last 20 years, work on absorption modeling has shifted almost exclusively to the compartmental framework. This report describes a new noncompartmental absorption modeling framework, the Lilly Absorption Modeling Platform (LAMP). LAMP connects a well-mixed stomach to a continuous tube model of the small intestine with plug flow. Within the continuous tube framework, the model includes intestinal mixing and a novel highly tunable precipitation model that can describe a combination of rapid nucleation and slow growth. The framework is designed to balance speed, consistency, and ease of use with a minimum of model complexity to capture the essential features of gastrointestinal (GI) physiology and critical elements of the oral absorption process. The model was validated based on predictions of the fraction absorbed and the maximum absorbable dose for a set of Eli Lilly and Company clinical compounds.

A model discrimination workflow to develop fit for purpose kinetic models of new pharmaceutical compounds in early stages of drug development involving complex reaction networks with limited prior information and provision to run new experiments.

AIChE JournalVolume 67, Issue 5 e17271 ISSUE INFORMATION – TABLE OF CONTENTSFree Access Issue Information – Table of Contents First published: 09 April 2021 https://doi.org/10.1002/aic.17271AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume67, Issue5May 2021e17271 RelatedInformation

The importance of the Design Space (DS) definition lies in the assurance of quality, a key goal of Quality by Design, and in the broadening from a single acceptable operating point, to a collection of feasible operating regions. Therefore, properly defining the limits of the design space is vital to provide manufacturing flexibility and quality assurance. In this work, we propose new MINLP reformulations to the extended flexibility analysis, where we distinguish between process and model parameters, to efficiently calculate a new flexibility index for process parameters. This index defines a hyperrectangular operating region within a design space of a pharmaceutical process in terms of the process parameters and accounts for the uncertainty in the model parameters, described by hyperrectangle and ellipsoidal sets. We illustrate the application of these novel techniques on several examples. (C) 2021 Elsevier Ltd. All rights reserved.

Flexibility analysis is one approach to modeling decision making under uncertainty, where ensuring feasible operation over the entire range of variation of the uncertain parameters is the main concern. The traditional flexibility analysis technique is based on the assumption that the value of the uncertain parameters can be accurately estimated or measured during the operating stage. Extensions to the technique have been proposed to take into account different types of parameters such as process and model parameters. The application of the concepts of flexibility analysis has proven to be useful in the design space definition. Therefore, in this work, we propose new reformulations to the extended flexibility constraint to calculate a new flexibility index for the process parameters, which defines the design space of the pharmaceutical company, subject to the variability in model parameters. In addition, we address the problem of design centering in order to maximize the design space, and to determine the optimal nominal conditions.

Drug substance particle size is a critical propertyOral dosage forms affecting drug product performance. Smaller particles dissolve faster and may improve bioavailability of the drug as a result. Smaller particles are typically dispersed more uniformly, leading to lower inter-tablet potency variation. Unfortunately, smaller particles can also result in poor powder handling characteristics or other processing issues. For example, powders can fail to flow through hoppers or stick to tooling surfaces, leading to poor tablet weight uniformity or tablet appearance issues. During crystallization, smaller particles can also be difficult to filter from the crystallization media. This can lead to higher levels of residual solvents and other impurities. Particle design and size selection are therefore critical to achieving a balance between manufacturability, bioavailability, and content uniformityContent uniformity . In this chapter, the impact of particle size on bioavailability is introduced and the impact on content uniformityUniformity dosage units is considered in depth. Control of the variability of tablet potency is discussed in relation to the overall particle size distribution. The risk-based selection of a positive control such as a screen to limit the maximum allowable particle size is discussed in relation to the occurrence of rare, but highly super-potent tablets.

Purpose The purpose of the research described herein was to develop a kinetic model for quantifying the effects of conditional and compositional variations on non-covalent polymorphic and covalent chemical transformations of gabapentin. Methods Kinetic models that describe the relationship between polymorphs and degradation product in a series of sequential or parallel steps were devised based on analysis of the resultant concentration time profiles. Model parameters were estimated using non-linear regression and Bayesian methods and evaluated in terms of their quantitative relationship to compositional and conditional variations. Results The model was constructed in which co-milling gabapentin with excipients determined three physically-initial concentrations (II 0 *, II 0 and III 0 ) and one chemically-initial concentration (lactam 0 ). For chemical transitions, no humidity effect was present but the catalytic effects of excipients on the conversion of II and III➔lactam were observed. For physical transition, excipient primarily influenced the physical state transition of III➔II through its ability to interact with humidity. Conclusions This model was shown to be robust to quantitatively account for the effects of temperature, humidity and excipient on rate constants associated with kinetics for each physical and chemical transition.

In the previous work, a reduced-order population pharmacokinetic (PK) model was used within a Bayesian inference framework to predict individualized patient dosing regimens. It was shown that a reduced-order model was adequate for individualized dosing of gabapentin given a minimum number of plasma samples from the given patient. However, this purely empirical model could not explain why patients have such different dosing needs. Accordingly, in this work, we couple an advanced compartment and transit oral absorption model with a full physiologically based PK model parameterized using a two-level hierarchical Bayesian approach. The coupled model provides the capability to not only understand the variable oral absorption but also the disposition of the drug. The proposed model-based strategy to individualized dosing is applied to the dosing of gabapentin using the retrospective data from the literature. The computations show that a standard starting regimen of 300 mg every 8 h is not likely to result in efficacious dosing for a substantial proportion of patients. Additionally, the proposed approach was able to incorporate urine data in a seamless way to inform the extent of absorption without assuming that the data were perfect. The mechanistic absorption model elucidated that the uncertainty in absorption plays a role in the variability in exposure seen across the patient population. It has also been suggested that the apparent absorption site is likely not the full upper GI, but a very localized segment due to the transit times required to fit the data. In this study, model implementation and Markov chain Monte Carlo computations were performed using the CmdStan package.

A quantitative, model-based risk assessment process was evaluated using Bayesian parameter estimation to determine the posterior distribution of the probability of a model tablet formulation’s (gabapentin) ability to meet end-of-expiry stability criteria-based manufacturing controls. Experimental data was obtained from an FDA-supported, multi-year project that involved researchers at nine universities working collaboratively with industrial and governmental scientists under the leadership of the National Institute for Pharmaceutical Technology and Education (NITPE). The risk assessment process involved the development of a design space manufacturing model and shelf life stability model that shared stability-related critical quality attributes (CQAs). Monte Carlo simulations of the design space and shelf life models that uses model parameter uncertainty to estimate the probability of shelf life failure as a function of manufacturing control. The resultant linked design space and shelf life stability models were tested by comparing model predicted and observed long-term stability data generated under a variety of pilot scale production conditions.

The use of particle size distribution (PSD) similarity metrics and the development and incorporation of drug release predictions based on PSD properties into PBPK models for various drug administration routes may provide a holistic approach for evaluating the effect of PSD differences on in vitro drug release and bioavailability of disperse systems. The objectives of this study were to provide a rational approach for evaluating the utility of in vitro PSD comparators for predicting bioequivalence for subcutaneously administered test and reference drug emulsions. Two types of in vitro comparators for test and reference emulsion products were evaluated: PSD characterization comparators (overlap metrics, median, and span ratios) and release profile comparators (f2 and various fractional time ratios). A subcutaneous-input PBPK disposition model was developed to simulate blood concentration-time profiles of reference and test emulsion products and pharmacokinetic responses (e.g., AUC, Cmax, and Tmax) were used to determine bioequivalence. A pool of 10,440 pairs of test and reference products was simulated using Monte Carlo experiments. The PSD and release profile comparators were correlated to pass/fail bioequivalence metrics using logistical regression. Based on the use of single in vitro comparators, the f2 method was the best predictor of bioequivalence prediction. The use of combinations of f2 and PSD overlap comparators (e.g., OVL or PROB) improved bioequivalence prediction to about 90%. Simulation procedures used in this study demonstrated a process for developing reliable in vitro BE predictors.

Patient safety risk due to toxic degradation products is a potentially critical quality issue for a small group of useful drug substances. Although the pharmacokinetics of toxic drug degradation products may impact product safety, these data are frequently unavailable. The objective of this study is to incorporate the prediction capability of physiologically based pharmacokinetic (PBPK) models into a rational drug degradation product risk assessment procedure using a series of model drug degradants (substituted anilines). The PBPK models were parameterized using a combination of experimental and literature data and computational methods. The impact of model parameter uncertainty was incorporated into stochastic risk assessment procedure for estimating human safe exposure levels based on the novel use of a statistical metric called "PROB" for comparing probability that a human toxicity-target tissue exposure exceeds the rat exposure level at a critical no-observed-adverse-effect level. When compared with traditional risk assessment calculations, this novel PBPK approach appeared to provide a rational basis for drug instability risk assessment by focusing on target tissue exposure and leveraging physiological, biochemical, biophysical knowledge of compounds and species.