The authors describe a library of synthetic RNA control elements that provide programmable post-transcriptional regulation of gene expression in yeast. This toolkit is then used to study endogenous regulation of the ergosterol biosynthetic pathway.

Establishing bioequivalence (BE) for dermatological drug products by conducting comparative clinical end point studies can be costly and the studies may not be sufficiently sensitive to detect certain formulation differences. Quantitative methods and modeling, such as physiologically-based pharmacokinetic (PBPK) modeling, can support alternative BE approaches with reduced or no human testing. To enable PBPK modeling for regulatory decision making, models should be sufficiently verified and validated (V&V) for the intended purpose. This report illustrates the US

Bupropion's metabolism and the formation of hydroxybupropion in the liver by cytochrome P450 2B6 (CYP2B6) has been extensively studied; however, the metabolism and formation of erythro/threohydrobupropion in the liver and intestine by carbonyl reductases (CR) has not been well characterized. The purpose of this investigation was to compare the relative contribution of the two metabolism pathways of bupropion (by CYP2B6 and CR) in the subcellular fractions of liver and intestine and to identify the CRs responsible for erythro/threohydrobupropion formation in the liver and the intestine. The results showed that the liver microsome generated the highest amount of hydroxybupropion (Vmax = 131 pmol/min per milligram, Km = 87 μM). In addition, liver microsome and S9 fractions formed similar levels of threohydrobupropion by CR (Vmax = 98-99 pmol/min per milligram and Km = 186-265 μM). Interestingly, the liver has similar capability to form hydroxybupropion (by CYP2B6) and threohydrobupropion (by CR). In contrast, none of the intestinal fractions generate hydroxybupropion, suggesting that the intestine does not have CYP2B6 available for metabolism of bupropion. However, intestinal S9 fraction formed threohydrobupropion to the extent of 25% of the amount of threohydrobupropion formed by liver S9 fraction. Enzyme inhibition and Western blots identified that 11β-dehydrogenase isozyme 1 in the liver microsome fraction is mainly responsible for the formation of threohydrobupropion, and in the intestine AKR7 may be responsible for the same metabolite formation. These quantitative comparisons of bupropion metabolism by CR in the liver and intestine may provide new insight into its efficacy and side effects with respect to these metabolites.

Methylphenidate (MPH) is currently used to treat children with attention deficit hyperactivity disorder (ADHD). Several extended-release (ER) formulations characterized by a dual release process were developed to improve efficacy over an extended duration. In this study, a model-based approach using literature data was developed to: 1) evaluate the most efficient pharmacokinetic (PK) model to characterize the complex PK profile of MPH ER formulations; 2) provide PK endpoint metrics for comparing ER formulations; 3) define criteria for optimizing development of ER formulations using a convolution-based model linking in vitro release, in vivo release, and hour-by-hour behavioral ratings of ADHD symptoms; and 4) define an optimized trial design for assessing the activity of MPH in pediatric populations. The convolution-based model accurately described the complex PK profiles of a variety of ER MPH products, providing a natural framework for establishing an in vitro/in vivo correlation and for defining criteria for assessing comparative bioequivalence of MPH ER products.

The implementation of clinically relevant drug product specifications (CRDPS) depends on establishing a link between in vitro performance and in vivo exposure. The scientific community, including regulatory agencies, relies on biopharmaceutics tools on the in vitro performance side, while to enable the link to in vivo exposure, physiologically based pharmacokinetic (PBPK) modeling offers much promise. However, when it comes to PBPK applications in support of CRDPS, otherwise called physiologically based biopharmaceutics models (PBBM), the tools are not yet at the desired level. Currently, it is not possible to integrate detailed variations in chemistry, manufacturing and controls (CMC) attributes and parameters into these models in a way that can consistently predict their effect on local and systemic drug exposure. Specifically, to achieve the desired level, there is a need to advance the science and policy of PBBM. This manuscript summarizes the proceedings of a three-day workshop where the following themes were discussed: 1) Challenges in the development and implementation of in vitro biopredictive tools needed for successful mechanistic modeling; 2) Best practices in model development, verification and validation; and 3) Appropriate terminology (e.g., PBBM vs. PBPK models for biopharmaceutics applications) and applications of PBBM in support of drug product quality.

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This publication summarizes the proceedings of day 3 of a 3-day workshop on "Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development." Specifically, this publication discusses the current approaches in building clinical relevance into drug product development for solid oral dosage forms, along with challenges that both industry and regulatory agencies are facing in setting clinically relevant drug product specifications (CRDPS) as presented at the workshop. The concept of clinical relevance is a multidisciplinary effort which implies an understanding of the relationship between the critical quality attributes (CQAs) and their impact on predetermined clinical outcomes. Developing this level of understanding, in many cases, requires introducing deliberate but meaningful variations into the critical material attributes (CMAs) and critical process parameters (CPPs) to establish a relationship between the resulting in vitro dissolution/release profiles and in vivo PK performance, a surrogate for clinical outcomes. Alternatively, with the intention of improving the efficiency of the drug product development process by limiting the burden of conducting in vivo studies, this understanding can be either built, or at least enhanced, through in silico efforts, such as IVIVC and physiologically based pharmacokinetic (PBPK) absorption modeling and simulation (M&S). These approaches enable dissolution testing to establish safe boundaries and reject drug product batches falling outside of the established safe range (e.g., due to inadequate in vivo performance) enabling the method to become clinically relevant. Ultimately, these efforts contribute towards patient-centric drug product development and allow regulatory flexibility throughout the lifecycle of the drug product.

The programming of cellular networks to achieve new biological functions depends on the development of genetic tools that link the presence of a molecular signal to gene-regulatory activity. Recently, a set of engineered RNA controllers was described that enabled predictable tuning of gene expression in the yeast Saccharomyces cerevisiae through directed cleavage of transcripts by an RNase III enzyme, Rnt1p. Here, we describe a strategy for building a new class of RNA sensing-actuation devices based on direct integration of RNA aptamers into a region of the Rnt1p hairpin that modulates Rnt1p cleavage rates. We demonstrate that ligand binding to the integrated aptamer domain is associated with a structural change sufficient to inhibit Rnt1p processing. Three tuning strategies based on the incorporation of different functional modules into the Rnt1p switch platform were demonstrated to optimize switch dynamics and ligand responsiveness. We further demonstrated that these tuning modules can be implemented combinatorially in a predictable manner to further improve the regulatory response properties of the switch. The modularity and tunability of the Rnt1p switch platform will allow for rapid optimization and tailoring of this gene control device, thus providing a useful tool for the design of complex genetic networks in yeast.