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De novo fatty acid biosynthesis in humans is accomplished by a multidomain protein, the type I fatty acid synthase (FAS). Although ubiquitously expressed in all tissues, fatty acid synthesis is not essential in normal healthy cells due to sufficient supply with fatty acids by the diet. However, FAS is overexpressed in cancer cells and correlates with tumor malignancy, which makes FAS an attractive selective therapeutic target in tumorigenesis. Herein, we present a crystal structure of the condensing part of murine FAS, highly homologous to human FAS, with octanoyl moieties covalently bound to the transferase (MAT) and the condensation (KS) domain. The MAT domain binds the octanoyl moiety in a novel (unique) conformation, which reflects the pronounced conformational dynamics of the substrate binding site responsible for the MAT substrate promiscuity. In contrast, the KS binding pocket just subtly adapts to the octanoyl moiety upon substrate binding. Besides the rigid domain structure, we found a positive cooperative effect in the substrate binding of the KS domain by a comprehensive enzyme kinetic study. These structural and mechanistic findings contribute significantly to our understanding of the mode of action of FAS and may guide future rational inhibitor designs.
The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification
(2019)
We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.
YEATS-domain-containing MLLT1 is an acetyl/acyl-lysine reader domain, which is structurally distinct from well-studied bromodomains and has been strongly associated in development of cancer. Here, we characterized piperazine-urea derivatives as an acetyl/acyl-lysine mimetic moiety for MLLT1. Crystal structures revealed distinct interaction mechanisms of this chemotype compared to the recently described benzimidazole-amide based inhibitors, exploiting different binding pockets within the protein. Thus, the piperazine-urea scaffold offers an alternative strategy for targeting the YEATS domain family.
We propose a generalized modeling framework for the kinetic mechanisms of transcriptional riboswitches. The formalism accommodates time-dependent transcription rates and changes of metabolite concentration and permits incorporation of variations in transcription rate depending on transcript length. We derive explicit analytical expressions for the fraction of transcripts that determine repression or activation of gene expression, pause site location and its slowing down of transcription for the case of the (2’dG)-sensing riboswitch from Mesoplasma florum. Our modeling challenges the current view on the exclusive importance of metabolite binding to transcripts containing only the aptamer domain. Numerical simulations of transcription proceeding in a continuous manner under time-dependent changes of metabolite concentration further suggest that rapid modulations in concentration result in a reduced dynamic range for riboswitch function regardless of transcription rate, while a combination of slow modulations and small transcription rates ensures a wide range of finely tuneable regulatory outcomes.
RNA-sequencing analyses are often limited to identifying lowest p-value transcripts, which does not address polygenic phenomena. To overcome this limitation, we developed an integrative approach that combines large scale transcriptomic meta-analysis of patient brain tissues with single-cell sequencing data of CNS neurons, short RNA-sequencing of human male- and female-originated cell lines, and connectomics of transcription factor- and microRNA-interactions with perturbed transcripts. We used this pipeline to analyze cortical transcripts of schizophrenia and bipolar disorder patients. While these pathologies show massive transcriptional parallels, their clinically well-known sexual dimorphisms remain unexplained. Our method explicates the differences between afflicted men and women, and identifies disease-affected pathways of cholinergic transmission and gp130-family neurokine controllers of immune function, interlinked by microRNAs. This approach may open new perspectives for seeking biomarkers and therapeutic targets, also in other transmitter systems and diseases.
Supersaturating formulations are widely used to improve the oral bioavailability of poorly soluble drugs. However, supersaturated solutions are thermodynamically unstable and such formulations often must include a precipitation inhibitor (PI) to sustain the increased concentrations to ensure that sufficient absorption will take place from the gastrointestinal tract. Recent advances in understanding the importance of drug-polymer interaction for successful precipitation inhibition have been encouraging. However, there still exists a gap in how this newfound understanding can be applied to improve the efficiency of PI screening and selection, which is still largely carried out with trial and error-based approaches. The aim of this study was to demonstrate how drug-polymer mixing enthalpy, calculated with the Conductor like Screening Model for Real Solvents (COSMO-RS), can be used as a parameter to select the most efficient precipitation inhibitors, and thus realise the most successful supersaturating formulations. This approach was tested for three different Biopharmaceutical Classification System (BCS) II compounds: dipyridamole, fenofibrate and glibenclamide, formulated with the supersaturating formulation, mesoporous silica. For all three compounds, precipitation was evident in mesoporous silica formulations without a precipitation inhibitor. Of the nine precipitation inhibitors studied, there was a strong positive correlation between the drug-polymer mixing enthalpy and the overall formulation performance, as measured by the area under the concentration-time curve in in vitro dissolution experiments. The data suggest that a rank-order based approach using calculated drug-polymer mixing enthalpy can be reliably used to select precipitation inhibitors for a more focused screening. Such an approach improves efficiency of precipitation inhibitor selection, whilst also improving the likelihood that the most optimal formulation will be realised.
Objectives: The objective of this review is to provide an overview of PK/PD models, focusing on drug-specific PK/PD models and highlighting their value-added in drug development and regulatory decision-making.
Key findings: Many PK/PD models, with varying degrees of complexity and physiological understanding, have been developed to evaluate the safety and efficacy of drug products. In special populations (e.g. pediatrics), in cases where there is genetic polymorphism and in other instances where therapeutic outcomes are not well described solely by PK metrics, the implementation of PK/PD models is crucial to assure the desired clinical outcome. Since dissociation between the pharmacokinetic and pharmacodynamic profiles is often observed, it is proposed that physiologically-based pharmacokinetic (PBPK) and PK/PD models be given more weight by regulatory authorities when assessing the therapeutic equivalence of drug products.
Summary: Modeling and simulation approaches already play an important role in drug development. While slowly moving away from “one-size fits all” PK methodologies to assess therapeutic outcomes, further work is required to increase confidence in PK/PD models in translatability and prediction of various clinical scenarios to encourage more widespread implementation in regulatory decision-making.