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Pharmaceutical Forced Degradation (Stress Testing) Endpoints: A Scientific Rationale and Industry Perspective.
No full textForced degradation (i.e., stress testing) of small molecule drug substances and products is a critical part of the drug development process, providing insight into the intrinsic stability of a drug that is foundational to the development and validation of stability-indicating analytical methods. There is a lack of clarity in the scientific literature and regulatory guidance as to what constitutes an "appropriate" endpoint to a set of stress experiments. That is, there is no clear agreement regarding how to determine if a sample has been sufficiently stressed. Notably, it is unclear what represents a suitable justification for declaring a drug substance (DS) or drug product (DP) "stable" to a specific forced degradation condition. To address these concerns and to ensure all pharmaceutically-relevant, potential degradation pathways have been suitably evaluated, we introduce a two-endpoint classification designation supported by experimental data. These two endpoints are 1) a % total degradation target outcome (e.g., for "reactive" drugs) or, 2) a specified amount of stress, even in the absence of any degradation (e.g., for "stable" drugs). These recommended endpoints are based on a review of the scientific literature, regulatory guidance, and a forced degradation data set from ten global pharmaceutical companies. The experimental data set, derived from the Campbell et al. (2022) benchmarking study,1 provides justification for the recommendations. Herein we provide a single source reference for small molecule DS and DP forced degradation stress conditions and endpoint best practices to support regulatory submissions (e.g., marketing applications). Application of these forced degradation conditions and endpoints, as part of a well-designed, comprehensive and a sufficiently rigorous study plan that includes both the DS and DP, provides comprehensive coverage of pharmaceutically-relevant degradation and avoids unreasonably extreme stress conditions and drastic endpoint recommendations sometimes found in the literature
Fiber alignment in 3D collagen networks as a biophysical marker for cell contractility.
No full textCells cultured in 3D fibrous biopolymer matrices exert traction forces on their environment that induce deformations and remodeling of the fiber network. By measuring these deformations, the traction forces can be reconstructed if the mechanical properties of the matrix and the force-free matrix configuration are known. These requirements limit the applicability of traction force reconstruction in practice. In this study, we test whether force-induced matrix remodeling can instead be used as a proxy for cellular traction forces. We measure the traction forces of hepatic stellate cells and different glioblastoma cell lines and quantify matrix remodeling by measuring the fiber orientation and fiber density around these cells. In agreement with simulated fiber networks, we demonstrate that changes in local fiber orientation and density are directly related to cell forces. By resolving Rho-kinase (ROCK) inhibitor-induced changes of traction forces, fiber alignment, and fiber density in hepatic stellate cells, we show that the method is suitable for drug screening assays. We conclude that differences in local fiber orientation and density, which are easily measurable, can be used as a qualitative proxy for changes in traction forces. The method is available as an open-source Python package with a graphical user interface
Platform Strategies for Synthetic Oligonucleotides
No full textSynthetic therapeutic oligonucleotide drug substances are commonly manufactured using the same basic unit operations, i.e., solid phase oligonucleotide synthesis, cleavage and deprotection, purification, and isolation. The platform nature of oligonucleotide drug substance manufacturing provides opportunities to optimize process development, streamline process validation, and expedite regulatory submissions. In this publication, we discuss a modular approach to platform strategies for synthetic oligonucleotide drug substances, the opportunities associated with the application of platform knowledge, and how such information could be presented in regulatory filings
Accelerating Biocatalysis Discovery with Machine Learning: A New Era in Enzyme Engineering new title based on editors feedback: Accelerating Biocatalysis Discovery with Machine Learning: A Paradigm Shift in Enzyme Engineering, Discovery, and Design
No full textNew emerging computational tools promise to revolutionize protein engineering for biocatalysis application and accelerate the development timelines previously needed to optimize an enzyme to its more efficient variant. For over a decade, the benefits of predictive algorithms have helped scientists and engineers to navigate the complexity of functional protein sequence space. More recently, spurred by dramatic advances in underlying computational tools, the promise of faster, cheaper, and more accurate enzyme identification, characterization and engineering has catapulted terms such as artificial intelligence and machine learning to the must-have vocabulary in the field. This perspective aims to discuss and also to celebrate these innovative new approaches in protein science by highlighting their potential on selected recent developments and applications and offering thoughts on future opportunities. It also critically assesses the technology’s limitations, unanswered questions and unmet challenges
Unexpected dynamics in femtomolar complexes of binding proteins with peptides.
No full textUltra-tight binding is usually observed for proteins associating with rigidified molecules. Previously, we demonstrated that femtomolar binders derived from the Armadillo repeat proteins (ArmRPs) can be designed to interact very tightly with fully flexible peptides. Here we show for ArmRPs with four and seven sequence-identical internal repeats that the peptide-ArmRP complexes display conformational dynamics. These dynamics stem from transient breakages of individual protein-residue contacts that are unrelated to overall unbinding. The labile contacts involve electrostatic interactions. We speculate that these dynamics allow attaining very high binding affinities, since they reduce entropic losses. Importantly, only NMR techniques can pick up these local events by directly detecting conformational exchange processes without complications from changes in solvent entropy. Furthermore, we demonstrate that the interaction surface of the repeat protein regularizes upon peptide binding to become more compatible with the peptide geometry. These results provide novel design principles for ultra-tight binders
Integrated analysis of the transcriptome and its interaction with the metabolome in metabolic associated fatty liver disease: Gut microbiome signatures, correlation networks, and effect of PNPLA3 genotype.
No full textInteractions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH
Integrated analysis of the transcriptome and its interaction with the metabolome in metabolic associated fatty liver disease: Gut microbiome signatures, correlation networks, and effect of PNPLA3 genotype.
No full textInteractions between communities of the gut microbiome and with the host could affect the onset and progression of metabolic associated fatty liver disease (MAFLD), and can be useful as new diagnostic and prognostic biomarkers. In this study, we performed a multi-omics approach to unravel gut microbiome signatures from 32 biopsy-proven patients (10 simple steatosis -SS- and 22 steatohepatitis -SH-) and 19 healthy volunteers (HV). Human and microbial transcripts were differentially identified between groups (MAFLD vs. HV/SH vs. SS), and analyzed for weighted correlation networks together with previously detected metabolites from the same set of samples. We observed that expression of Desulfobacteraceae bacterium, methanogenic archaea, Mushu phage, opportunistic pathogenic fungi Fusarium proliferatum and Candida sorbophila, protozoa Blastocystis spp. and Fonticula alba were upregulated in MAFLD and SH. Desulfobacteraceae bacterium and Mushu phage were hub species in the onset of MAFLD, whereas the activity of Fonticula alba, Faecalibacterium prausnitzii, and Mushu phage act as key regulators of the progression to SH. A combination of clinical, metabolomic, and transcriptomic parameters showed the highest predictive capacity for MAFLD and SH (AUC = 0.96). In conclusion, faecal microbiome markers from several community members contribute to the switch in signatures characteristic of MAFLD and its progression towards SH
Glagov revisited: coronary artery disease phenotype on non-invasive imaging provides rationale for implementing preventive pharmacotherapy-a case report.
No full textGlagov et al. showed that no reduction in vessel lumen occurred until the atherosclerotic plaque burden exceeded 40% of the vessel area. Most major adverse cardiac events occurring in the first 4 years after a myocardial infarction arise from untreated angiographically mild, non-flow-limiting lesions at the time of the index event. We report how computed tomography (CT) coronary angiography (CCTA) can be used to non-invasively risk stratify a patient with non-obstructive coronary artery disease (CAD) and guide further management.A 69-year-old non-smoking female with hypertension, dyslipidaemia, and hypothyroidism presented with atypical chest pain. Electrocardiogram and left ventricular ejection fraction were normal. Her lipidic profile was normal. CCTA showed a lipid-rich plaque with very low attenuation (<30 HU) in the left main stem (LMS) extending into the proximal left anterior descending (LAD) and in the mid LAD artery. The maximum plaque burden in the LMS was 67% with a remodelling index of 1.375, and an area stenosis of 22%. Tissue characterization showed a lipid-rich plaque with a thin fibrous cap. The perivascular fat attenuation index (FAI) in the proximal LAD was suggestive of (-69 HU) inflamed perivascular fat. Shear stress analysis of the LMS plaque showed normal wall shear stress (WSS); however, the axial plaque stress was high. Her medications were intensified to rosuvastatin 20 mg once daily (OD) and ezetimibe 10 mg OD. The patient remained asymptomatic at 6 months follow-up.Our case exemplifies the value of CCTA as a diagnostic 'one-stop shop' (CCTA, finite element analysis, computed tomographic density [CTD], tissue characterization analysis, FAI analysis, WSS and wall strain, and etc.) when stratifying a patient with non-obstructive CAD. With further development of novel potent anti-lipidaemic and anti-inflammatory drugs, non-obstructive lesions with adverse plaque and haemodynamic parameters will have the opportunity to be treated with additional preventive pharmacological therapy
Industry Perspective on Therapeutic Peptide Drug-Drug Interaction Assessments During Drug Development: A European Federation of Pharmaceutical Industries and Associations White Paper.
No full textDrug-drug interaction (DDI) assessments are well defined in health authority guidelines for small molecule drugs, and US Food and Drug Administration (FDA) draft guidance is now available for therapeutic proteins. However, there are currently no regulatory guidelines outlining DDI assessments for therapeutic peptides, which poses significant uncertainty and challenges during drug development for this heterogenous class of molecules. A cross-industry peptide DDI working group consisting of experts from 10 leading companies was formed under the sponsorship of the European Federation of Pharmaceutical Industries and Associations. We aimed to capture the range of DDI studies undertaken for peptide drugs by (i) anonymously surveying relevant companies involved in peptide drug development to better understand DDI study type/timing currently performed and (ii) compiling a database containing in vitro / clinical DDI data from submission packages for recently approved peptide drugs. Our analyses highlight significant gaps and uncertainty in the field. For example, the reported timing of in vitro peptide DDI studies, if performed, vary substantially across responding companies from early research to phase III. Nearly all in vitro cytochrome P450 / transporter inhibition studies reported in the survey were negative. For the few positive hits reported, no clinical follow-up studies were performed, questioning the clinical relevance of these findings. Furthermore, available submission packages reveal DDI likelihood is low for peptides >2 kDa, making it reasonable to adopt a risk-based approach during drug development for larger peptides. By benchmarking the landscape of peptide DDI activities across the industry, we set the stage for future discussions with health authorities on harmonizing peptide DDI approaches