A5076682119- Computational Drug Discovery Methods
- Biosimilars and Bioanalytical Methods
- Numerical methods in engineering
- Viral Infectious Diseases and Gene Expression in Insects
- Metabolomics and Mass Spectrometry Studies
- Protein purification and stability
- Cardiac electrophysiology and arrhythmias
- Machine Learning in Materials Science
- Analytical Chemistry and Chromatography
- Fatigue and fracture mechanics
- Mechanical Behavior of Composites
- Monoclonal and Polyclonal Antibodies Research
- Pluripotent Stem Cells Research
- Elasticity and Wave Propagation
- Pharmacogenetics and Drug Metabolism
- Neuroscience and Neural Engineering
- Biomedical and Engineering Education
- Innovative Microfluidic and Catalytic Techniques Innovation
- Geotechnical and Geomechanical Engineering
- VLSI and Analog Circuit Testing
- Chemotherapy-induced cardiotoxicity and mitigation
- Animal testing and alternatives
- Spectroscopy and Chemometric Analyses
- Contact Mechanics and Variational Inequalities
- 3D Printing in Biomedical Research
AstraZeneca (United Kingdom)
2021-2024
Green Biologics (United Kingdom)
2015-2022
Lonza (United Kingdom)
2015-2022
Lonza (Switzerland)
2015
Optibrium (United Kingdom)
2009-2010
Biocompatibles (United Kingdom)
2008
University of Cambridge
2002-2007
Mundipharma (United Kingdom)
2006-2007
In this article, we discuss the application of Gaussian Process method for prediction absorption, distribution, metabolism, and excretion (ADME) properties. On basis a Bayesian probabilistic approach, is widely used in field machine learning but has rarely been applied quantitative structure−activity relationship ADME modeling. The suitable modeling nonlinear relationships, does not require subjective determination model parameters, works large number descriptors, inherently resistant to...
Aggregation is a common problem affecting biopharmaceutical development that can have significant effect on the quality of product, as well safety to patients, particularly because increased risk immune reactions. Here, we describe new high-throughput screening algorithm developed classify antibody molecules based their propensity aggregate. The tool, constructed and validated experimental aggregation data for over 500 antibodies, able discern with high defined by criteria relevant...
Prior to clinical development, a comprehensive pharmacokinetic characterization of novel drug is required understand its exposure at the site action and elimination. Accordingly, in vitro assays animal studies are regularly employed predict humans, which often costly time-consuming. For this reason, prediction human pharmacokinetics point design would be high value for discovery. Therefore, we have established data curation protocol that enables machine learning evaluation 12 vivo parameters...
Animal pharmacokinetic (PK) data as well human and animal in vitro systems are utilized drug discovery to define the rate route of elimination. Accurate prediction mechanistic understanding clearance disposition animals provide a degree confidence for extrapolation humans. In addition, vivo properties can be used improve design during discovery, help select compounds with better properties, reduce number experiments. this study, we generated machine learning models able predict rat PK...
In this article, we extend the application of Gaussian processes technique to classification quantitative structure-activity relationship modeling problems. We explore two approaches, an intrinsic and a probit treatment regression method. Here, describe basic concepts methods apply these techniques building category models absorption, distribution, metabolism, excretion, toxicity target activity data. also compare performance for other known computational methods, namely decision trees,...
Abstract In this article, we review recent developments in the prediction of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties by Quantitative Structure–Activity Relationships (QSAR). We consider advances statistical modelling techniques, molecular descriptors sets data used for model building changes way which predictive ADMET models are being applied drug discovery. also discuss current challenges that remain to be addressed. While there has been progress...
ADMET Models, whether in silico or vitro, are commonly used to 'profile' molecules, identify potential liabilities filter out molecules expected have undesirable properties. While useful, this is the most basic application of such models. Here, we will show how models may be go 'beyond profiling' guide key decisions drug discovery. For example, selection chemical series focus resources with confidence design improved targeting structural modifications improve To prioritise and series,...
Abstract Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, combined machine learning methods with modified calcium transient assay human-induced pluripotent stem cell-derived cardiomyocytes identify nine parameters could predict Next, applied transcriptomic profiling human cardiac microtissues exposed structural and...
Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use waveform parameters predict activity merit deeper investigation. In this study, we derived, evaluated, applied 38 in a novel Python framework, including (among others) peak frequency, amplitude, widths, parameter, shoulder-tail...
Translation initiation is on the critical pathway for production of monoclonal antibodies (mAbs) by mammalian cells. Formation a closed loop structure comprised mRNA, number eukaryotic factors (eIFs) and ribosomal proteins has been proposed to aid re-initiation translation therefore increase global translational efficiency. We have determined mRNA protein levels key components loop, eIFs (eIF3a, eIF3b, eIF3c, eIF3h, eIF3i eIF4G1), poly(A)-binding (PABP) 1 PABP-interacting (PAIP1), across...
Functional changes to cardiomyocytes are undesirable during drug discovery and identifying the inotropic effects of compounds is hence necessary decrease risk cardiovascular adverse in clinic. Recently, approaches leveraging calcium transients human induced pluripotent stem cell-derived (hiPSC-CMs) have been developed detect contractility changes, by a variety mechanisms early projects. Although these able provide some predictive ability, we hypothesised that using additional waveform...
Steady-state intersonic propagation of a shear crack is considered, with the admission cohesion across faces. The asymptotic limit "small-scale cohesion". which occurs when magmitude cohesive stress far exceeds that applied stress, developed explicitly to obtain criterion ot "Barenblatt" type. application this requires only calculation "applied" intensity coefficient disregarded: an equation motion follows by equating modulus cohesion" depends on model employed. An explicit formula for...
Abstract ChemInform is a weekly Abstracting Service, delivering concise information at glance that was extracted from about 200 leading journals. To access Abstract, please click on HTML or PDF.