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AspSnFR: A genetically encoded biosensor for real-time monitoring of aspartate in live cells
No full textAspartate is crucial for nucleotide synthesis, ammonia detoxification, and maintaining redox balance via the
malate-aspartate-shuttle (MAS). To disentangle these multiple roles of aspartate metabolism, tools are
required that measure aspartate concentrations in real time and in live cells. We introduce AspSnFR, a genetically
encoded green fluorescent biosensor for intracellular aspartate, engineered through displaying and
screening biosensor libraries on mammalian cells. In live cells, AspSnFR is able to precisely and quantitatively
measure cytosolic aspartate concentrations and dissect its production from glutamine. Combining
high-content imaging of AspSnFR with pharmacological perturbations exposes differences in metabolic vulnerabilities
of aspartate levels based on nutrient availability. Further, AspSnFR facilitates tracking of aspartate
export from mitochondria through SLC25A12, the MAS’ key transporter. We show that SLC25A12 is a
rapidly responding and direct route to couple Ca2+ signaling withmitochondrial aspartate export. This establishes
SLC25A12 as a crucial link between cellular signaling, mitochondrial respiration, and metabolism
An Amalgamation of LC-MS-HDX and Knowledge-based Software for Ameliorating Structure Prediction of Drug Degradation Products
No full textStructure identification of drug degradation products is one of the key components of any pharmaceutical product development. Knowledge-based in-silico tools are widely used to predict the probable degradation and excipient interaction products. However, multiple structures of the same masses are predicted through this software, which makes identification of the correct/exact structure of the degradation product difficult. In this study, the utilization of a simple yet powerful analytical tool, LC-MS HDX was explored for improving the predictive capability of the in-silico tool. For the same, 5 drugs were selected as representative of pharmaceutical development workflow and subjected to a stress degradation study as per ICH regulatory requirements. There were in total 55 degradation products, for which all possible structures were predicted through Zeneth® software. The number of labile hydrogens in each of the 55 products was obtained through a simple LC-MS HDX method. While scrutinizing predicted structures with the number of labile hydrogens, many false positive predictions could be ruled out. Out of 55 degradation products, this approach has helped in improving the predictability of 45 products corresponding to ∼82% cases. It is simple to adopt for the industry by simply amalgamating analytical practices with AI prediction
Patient-centric Comparability Assessment of Biopharmaceuticals
No full textThe comparability assessment of a biological product after implementing a manufacturing process change should involve a risk-based approach. Process changes may occur at any stage of the product lifecycle: early development, clinical manufacture for pivotal trials, or post-approval. The risk of the change to impact product quality varies, and the design of the comparability assessment should be adapted accordingly.
A working group reviewed and consolidated industry approaches to assess comparability of traditional protein-based biological products during clinical development and post-approval. The insights compiled in this review article can be leveraged across the industry and encompass topics such as a risk-evaluation strategy, the design of comparability studies, definition of assessment criteria for comparability, holistic evaluation of data, and the regulatory submission strategy. These practices may help companies in design and execution of comparability assessments, and they may inform discussions with global regulators
Ten Years of the Manufacturing Classification System: A review of literature applications and an extension of the framework to continuous manufacture.
No full textThe MCS initiative was first introduced in 2013. Since then, two MCS papers have been published: the first proposing a structured approach to consider the impact of drug substance physical properties on manufacturability and the second outlining real world examples of MCS principles. By 2023, both publications had been extensively cited by over 240 publications. This article firstly reviews this citing work and consider how the MCS concepts have been received and are being applied. Secondly, we will extend the MCS framework to continuous manufacture.The review structure follows the flow of drug product development focussing first on optimisation of API properties. The exploitation of links between API particle properties and manufacturability using large datasets seems particularly promising. Subsequently, applications of the MCS for formulation design include a detailed look at the impact of percolation threshold, the role of excipients and how other classification systems can be of assistance. The final review section focusses on manufacturing process development, covering the impact of strain rate sensitivity and modelling applications.The second part of the paper focuses on continuous processing proposing a parallel MCS framework alongside the existing batch manufacturing guidance. Specifically, we propose that continuous direct compression can accommodate a wider range of API properties compared to its batch equivalent
Clinical and molecular profiling of human visceral adipose tissue reveals impairment of vascular architecture and remodeling as an early hallmark of dysfunction.
No full textAdipose tissue dysfunction is more related to insulin resistance than body mass index itself and an alteration in adipose tissue function is thought to underlie the shift from metabolically healthy to unhealthy obesity. Herein, we performed a clustering analysis that revealed distinct visceral adipose tissue gene expression patterns in patients with obesity at distinct stages of metabolic dysregulation. We have built a cross-sectional cohort that aims at reflecting the evolution of the metabolic sequelae of obesity with the main objective to map the sequential events that play a role in adipose tissue dysfunction from the metabolically healthy (insulin-sensitive) state to several incremental degrees of metabolic dysregulation, encompassing insulin resistance establishment, pre-diabetes, and type 2 diabetes. We found that insulin resistance is mainly marked by the downregulation of adipose tissue vasculature remodeling-associated gene expression, suggesting that processes like angiogenesis and adaptative expansion/retraction ability suffer early dysregulation. Prediabetes was characterized by compensatory growth factor-dependent signaling and increased response to hypoxia, while type 2 diabetes was associated with loss of cellular response to insulin and hypoxia and concomitant upregulation of inflammatory markers. Our findings suggest a putative sequence of dysregulation of biological processes that is not linear and has multiple distinct phases across the metabolic dysregulation process, ultimately culminating in the climax of adipose tissue dysfunction in type 2 diabetes. Several studies have addressed the transcriptomic changes in adipose tissue of patients with obesity. However, to the best of our knowledge, this is the first study unraveling the potential molecular mechanisms associated with the multi-step evolution of adipose tissue dysfunction along the metabolic sequelae of obesity
First-in-human, Randomized, Double-Blind, Placebo-Controlled, Single and Multiple Ascending Doses Clinical Study to Assess the Safety, Tolerability, and Pharmacokinetics of KAE609 Administered Intravenously in Healthy Adults
No full textThis first-in-human study assessed the safety, tolerability, and pharmacokinetics (PK) of cipargamin administered intravenously in healthy adults. The study included two parts, Part 1 single ascending dose (SAD: 10.5 mg to 210 mg; n=8 [Active: 6, Placebo: 2] in each cohort) and Part 2 multiple ascending dose (MAD: 60 mg daily and 120 mg daily for 5 days; n=9 [Active: 6, Placebo: 3] in each cohort). The follow-up period after the last dose was on Days 3, 4 and 6 for SAD while it was Days 7, 8 and 10 for MAD. Safety and PK parameters were reviewed at the completion of each cohort prior to the dosing of subsequent cohort. This single and multi-dose study explored its use for clinical development in severe malaria patients.
In the SAD part, an increase in systemic exposure (maximum measured concentration and area under the curve) was observed with increasing dose from 10.5−210 mg post single intravenous dose of cipargamin. Cipargamin was eliminated with a mean T1/2 of 21.9−38.9 hour (h). There was a moderate volume of distribution (92.9−154 L) and low clearance (2.43−4.33 L/h) over the entire dose range.
In the MAD part, the mean accumulation ratio was 1.51 (cipargamin 60 mg) and 2.43 (cipargamin 120 mg) after once-daily administration for 5 days. After Day 5, the mean T1/2 was 35.5 h (cipargamin 60 mg) and 31.9 h (cipargamin 120 mg) with 2-fold increase in dose (60–120 mg) resulting in ~2-fold increased exposure.
Cipargamin was well-tolerated with commonly reported gastrointestinal, neurological, and genitourinary events of mild severity. The incidence of adverse events increased with increasing dose. Higher baseline corrected QTcF (∆QTcF) was seen with increasing exposure after single or multiple IV doses of cipargamin. However, an effect on ΔΔQTcF ≥10 ms can be ruled out at a concentration range studied.
KEYWORDS: Cipargamin, First-in-Human, Intravenous, KAE609, Malaria, Pharmacokinetic, Safety
ClinicalTrials.gov Identifier: NCT0432125
Balancing Molecular Size, Activity, Permeability, and Other Properties: Drug Candidates in the Context of Their Chemical Structure Optimization.
No full textChemical structure optimization is a vital part of early drug discovery projects. Starting with compounds that show activity on the target of interest, the chemical structures are subsequently optimized toward a development candidate (DC) molecule with the best chances of clinical success. However, the DCs in the context of such optimization programs, as well as detailed characterization of major limiting factors, have not been investigated in detail so far. Here, we report an analysis of the historical DC molecules at Novartis since 2005 in the context of their optimization projects. Mapping the DCs into their respective chemical optimization series, we find that these tend to be synthesized rather early in a substantial number of cases. Further analysis of structural properties, ADMET, and potency-related readouts revealed that DC compounds tend to be generally significantly smaller, more permeable, and have higher ligand efficiency than other compounds sent to in vivo PK studies, which we also show for compounds from the same chemical series. Although this might seem obvious to most practitioners in medicinal chemistry, for all of these properties, we could show that they tend to evolve in an undesired direction during structure optimization. This highlights the difficulty of successfully translating our knowledge to medicinal chemistry optimizations
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.
No full textMost drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential DDI issues, high-throughput in vitro assays were established to assess the TDI of CYP3A4 during the discovery and lead optimization phases. However, in silico machine learning models would enable an earlier and larger-scale assessment of TDI potential liabilities. For CYP inhibition, most modeling efforts have focused on highly imbalanced and small data sets. Moreover, assay variability is rarely considered, which is key to understand the model's quality and suitability for decision-making. In this work, machine learning models were built for the prediction of TDI of CYP3A4, evaluated prospectively, and compared to the variability of the experimental assay. Different modeling strategies were investigated to assess their influence on the model's performance. Through multitask learning, additional data sets were leveraged for model building, coming from public databases, in-house CYP-related assays, or other pharmaceutical companies (federated learning). Apart from the numerical prediction of inactivation rates of CYP3A4 TDI, three-class predictions were carried out, giving a negative (inactivation rate kobs 0.025 min-1) output. The final multitask graph neural network model achieved misclassification rates of 8 and 7% for positive and negative TDI, respectively. Importantly, the presented deep learning-based predictions had a similar precision to the reproducibility of in vitro experiments and thus offered great opportunities for drug design, early derisk of DDI potential, and selection of experiments. To facilitate CYP inhibition modeling efforts in the public domain, the developed model was used to annotate ∼16 000 publicly available structures, and a surrogate data set is shared as Supporting Information
All that glitters is not gold: Type-I error controlled variable selection from clinical trial data
No full textBeyond their primary purpose of establishing causal effects, clinical trial data can be used to identify prognostic measures of disease or biomarkers that predict treatment efficacy. Such endeavors can be interpreted as variable selection problems for which a plethora of machine learning algorithms has recently been developed. However, these algorithms are generally designed to optimize predictions and often only provide the measures used for variable selection, such as importance scores, as a by-product. Thus, without known operating characteristics or a mechanism for error control, these approaches contribute to the current replicability crisis. In the context of clinical development, this lack of control for false discoveries (type-I errors) can result in unnecessary research efforts, increased patient burden, and avoidable costs. Here, we review a recently proposed model-agnostic wrapper framework, the knockoff framework, which offers a robust approach to variable selection with guaranteed type-I error control. We explore the operating characteristics of various knockoff based variable selection methods under broad settings relevant to the analysis of clinical trial data, raising awareness for practical considerations. Furthermore, we introduce a novel knockoff generation method that addresses two main limitations of previously suggested methods relevant for clinical development settings, empirically obtaining tighter bounds on type-I error control and gaining an order of magnitude in computational efficiency in mixed data settings
Regulatory Issues of Platform Trials: Learnings from EU-PEARL.
No full textAlthough platform trials have many benefits, the complexity of these designs may result not only in increased methodological but also regulatory and ethical challenges. These aspects were addressed as part of the IMI project EU Patient-Centric Clinical Trial Platforms (EU-PEARL). We reviewed the available guidelines on platform trials in the European Union and the United States. This is supported and complemented by feedback received from regulatory interactions with the European Medicines Agency and the US Food and Drug Administration. Throughout the project we collected the needs of all relevant stakeholders including ethics committees, regulators, and health technology assessment bodies through active dialog and dedicated stakeholder workshops. Furthermore, we focused on methodological aspects and where applicable identified the corresponding guidance. Learnings from the guideline review, regulatory interactions, and workshops are provided. Based on these, a master protocol template was developed. Issues that still need harmonization or clarification in guidelines or where further methodological research is needed are also presented. These include questions around clinical trial submissions in Europe, the need for multiplicity control across the whole master protocol, the use of non-concurrent controls, and the impact of different randomization schemes. Master protocols are an efficient and patient-centered clinical trial design that can expedite drug development. However, they can also introduce additional operational and regulatory complexities. It is important to understand the different requirements of stakeholders upfront and address them in the trial. While relevant guidance is increasing, early dialog with relevant stakeholders can help to further support such designs