Optimizing Clinical Drug Product Performance: Applying Biopharmaceutics Risk Assessment Roadmap (BioRAM) and the BioRAM Scoring Grid.

The aim of Biopharmaceutics Risk Assessment Roadmap (BioRAM) and the BioRAM Scoring Grid is to facilitate optimization of clinical performance of drug products. BioRAM strategy relies on therapy-driven drug delivery and follows an integrated systems approach for formulating and addressing critical questions and decision-making (J Pharm Sci. 2014,103(11): 3777-97). In BioRAM, risk is defined as not achieving the intended in vivo drug product performance, and success is assessed by time to decision-making and action. Emphasis on time to decision-making and time to action highlights the value of well-formulated critical questions and well-designed and conducted integrated studies. This commentary describes and illustrates application of the BioRAM Scoring Grid, a companion to the BioRAM strategy, which guides implementation of such an integrated strategy encompassing 12 critical areas and 6 assessment stages. Application of the BioRAM Scoring Grid is illustrated using published literature. Organizational considerations for implementing BioRAM strategy, including the interactions, function, and skillsets of the BioRAM group members, are also reviewed. As a creative and innovative systems approach, we believe that BioRAM is going to have a broad-reaching impact, influencing drug development and leading to unique collaborations influencing how we learn, and leverage and share knowledge.

The biopharmaceutics risk assessment roadmap for optimizing clinical drug product performance.

The biopharmaceutics risk assessment roadmap (BioRAM) optimizes drug product development and performance by using therapy-driven target drug delivery profiles as a framework to achieve the desired therapeutic outcome. Hence, clinical relevance is directly built into early formulation development. Biopharmaceutics tools are used to identify and address potential challenges to optimize the drug product for patient benefit. For illustration, BioRAM is applied to four relatively common therapy-driven drug delivery scenarios: rapid therapeutic onset, multiphasic delivery, delayed therapeutic onset, and maintenance of target exposure. BioRAM considers the therapeutic target with the drug substance characteristics and enables collection of critical knowledge for development of a dosage form that can perform consistently for meeting the patient's needs. Accordingly, the key factors are identified and in vitro, in vivo, and in silico modeling and simulation techniques are used to elucidate the optimal drug delivery rate and pattern. BioRAM enables (1) feasibility assessment for the dosage form, (2) development and conduct of appropriate "learning and confirming" studies, (3) transparency in decision-making, (4) assurance of drug product quality during lifecycle management, and (5) development of robust linkages between the desired clinical outcome and the necessary product quality attributes for inclusion in the quality target product profile.

Mechanistic insights into the scale-up of the roller compaction process: a practical and dimensionless approach.

Although the roller compaction process appears simple, efforts to quantitatively model the process have proven challenging because of complex material behavior in the feeding and compaction zones. To date, implementation of roller compaction models to experimental work has been limited because these models typically require large experimental data sets or obscure input parameters that are difficult to obtain experimentally. In this work, an alternative approach has been established, expanding upon a widely used roller compaction model, Johanson's model, to enable its incorporation into a daily workflow. The proposed method requires only standard, routinely measured parameters as inputs. An excellent correlation between simulated and experimental results has been achieved for placebo and active blends up to 22% (w/w) drug load. Furthermore, a dimensionless relationship between key process parameters and final compact properties was elucidated. This dimensionless parameter, referred to as the modified Bingham number (Bm *), highlights the importance of balancing yield and viscous stresses during roller compaction to achieve optimal output properties. By maintaining a constant ratio of yield-to-viscous stresses, as indicated by a constant Bm *, consistent products were attained between two scales of operation. Bm * was shown to provide guidance toward determining the design space for formulation development, as well as to facilitate scale-up development.

Biowaiver approach for biopharmaceutics classification system class 3 compound metformin hydrochloride using in silico modeling.

The dependency of metformin in vivo disposition on the rate and extent of dissolution was studied. The analysis includes the use of fundamental principles of drug input, permeability, and intestinal transit time within the framework of a compartmental absorption transit model to predict key pharmacokinetic (PK) parameters and then compare the results to clinical data. The simulations show that the maximum plasma concentration (C(max) ) and area under the curve (AUC) are not significantly affected when 100% of drug is released within 2 h of oral dosing, which was confirmed with corresponding human PK data. Furthermore, in vitro dissolution profiles measured in aqueous buffers at pH values of 1.2, 4.5, and 6.8 were slower than in vivo release profiles generated by deconvolution of metformin products that were bioequivalent. On the basis of this work, formulations of metformin that release 100% in vitro in a time period equal to or less than two hours are indicated to be bioequivalent. The use of modeling offers a mechanistic-based approach for demonstrating acceptable bioperformance for metformin formulations without having to resort to in vivo bioequivalence studies and may be more robust than statistical comparison of in vitro release profiles. This work further provides a strategy for considering Biopharmaceutics Classification System (BCS) Class 3 compounds to be included under biowaiver guidelines as for BCS Class 1 compounds.

Predictive models for drugs exhibiting negative food effects based on their biopharmaceutical characteristics.

CONTEXT: A drug is defined to exhibit food effects if its pharmacokinetic parameter, area under the curve (AUC0₋∞) is different when co-administered with food in comparison with its administration on a fasted stomach. Food effects of drugs administered in immediate release dosage forms were classified as positive, negative, and no food effects. OBJECTIVE: In this study, predictive models for negative food effects of drugs that are stable in the gastrointestinal tract and do not complex with Ca²âº are reported. METHODS: An empirical model was developed using five drugs exhibiting negative food effects and seven drugs exhibiting no food effects by multiple regression analysis, based on biopharmaceutical properties generated from in vitro experiments. An oral absorption model was adopted for simulating negative food effects of model compounds using in situ rat intestinal permeability. RESULTS: Analysis of selected model drugs indicated that percent food effects correlated to their dissociation constant, K (K(a) or K(b)) and Caco-2 permeabilities. The obtained predictive equation was: Food effect (%)=(2.60 x 105·P(app))--(2.91 x 105·K)--8.50. Applying the oral absorption model, the predicted food effects matched the trends of published negative food effects when the two experimental pH conditions of fed and fasted state intestinal environment were used. CONCLUSION: A predictive model for negative food effects based on the correlation of food effects with dissociation constant and Caco-2 permeability was established and simulations of food effects using rat intestinal permeability supported the drugs? published negative food effects. Thus, an empirical and a mechanistic model as potential tools for predicting negative food effects are reported.

Investigation of some factors contributing to negative food effects.

A drug is defined as exhibiting negative food effects, if the co-administration of food statistically decreases its area under the curve, AUC, when compared with its administration on a fasted stomach. In this study, the role of biopharmaceutical factors that contribute to negative food effects was studied using furosemide, nadolol, tacrine and atenolol (as model compounds exhibiting negative food effects), and prednisolone, hydrochlorothiazide and ibuprofen (as model compounds that do not show any food effects). The physiological pH of the upper intestinal tract is lower, at pH 5, in the postprandial state when compared with the preprandial state, pH 6.5. Drugs that exhibited negative food effects had low apical to basolateral Caco-2 permeabilities, low pKa/pKb and Log P values of less than 1. The drugs exhibiting negative food effects had low distribution coefficients at the pH conditions of the fed and fasted states. Furosemide, being a hydrophilic, poorly soluble acidic drug showed lower solubility in the fed state when compared with the fasted state. Basic drugs, atenolol, nadolol and tacrine, are ionized to a higher extent in the fed state and exhibit lower permeability and lower absorption when compared with the fasted state. Thus, drugs were found to exhibit negative food effects owing to their decrease in solubility or permeability in the upper intestinal tract of the fed state when compared with the fasted state.