Two software houses providing bioinformatics and biosimulation for the life sciences and pharmaceutical industries – one based in the USA and one in Europe – have both announced that they are expanding into East Asia.
Certara has launched a strategic drug development consulting company in China. Certara Strategic Consulting China will be based in Shanghai and will deploy the full range of its parent company’s modelling and simulation expertise to make the drug development process in China safer and more efficient.
At the same time, Qlucore, which specialises in the development of bioinformatics software, has announced that it is targeting the Malaysian biotech and academic research markets through new partnership with Genomax. This company was founded in 2007 to provide the life science research communities and medical institutions in Malaysia and beyond with innovative products and services to accelerate biodiscovery.
Certara’s interest in China follows the recent introduction by the China Food and Drug Administration of new procedures for drug registration and approval. Globally, Certara Strategic Consulting provides outsourced drug discovery and development modelling and simulation, and strategic pharmacometric services to more than 100 biopharmaceutical companies, non-profit foundations, and regulatory agencies worldwide.
‘We are just starting to see the profound impact that biosimulation, model-based meta-analysis, and comparative effectiveness analysis can have on a drug candidate’s development, regulatory review, and clinical application. Certara can assist clients in maximising their efforts at each of these stages,’ said Dr Yuying Gao. Dr Gao is President and CEO of the new company. She is a former vice-president of consulting services at Quantitative Solutions, a global pharmacometrics consulting company, which merged with Certara’s Pharsight Consulting Services in July 2015 to form Certara Strategic Consulting.
Because Genomax already has experience in the field of bioinformatics software, Qlucore is ‘confident that this cooperation will help researchers in Malaysia to learn more about our software, and to achieve extraordinary results with their data analysis," according to Carl-Johan Ivarsson, the company’s president. Qlucore Omics Explorer is highly intuitive and thus allows researchers – the people with the most biological insight – to study their own data and to look for patterns and structures. They do not need to be statistics or computer experts in order to use the software effectively.
Qlucore started as a collaborative research project at Lund University, Sweden, supported by researchers at the Departments of Mathematics and Clinical Genetics, in order to address the vast amount of high-dimensional data generated with microarray gene expression analysis. The result is a core software engine that lets the user handle and filter data and the same time instantly visualise it in 3D. This will aid the user in identifying hidden structures and patterns. Over the past four years major efforts have been made to optimise and develop software that is extremely fast, allowing the user to explore and analyse high-dimensional data sets with the use of a normal PC, interactively and in real time.
Qlucore Omics Explorer Version 3.2 has just been launched, so researchers will be able to undertake deeper data exploration and biomarker discovery using the additional functionalities for clustering and classification.
Clustering is used to find subgroups among data samples and to see whether the samples naturally distribute themselves into distinct clusters. The new clustering functionality will enhance both the data exploration and statistical verification modes of the program. It will further assist the user to find clusters and subgroups in data, and complements existing functionalities such as projection score, variance filter and PCA plots.
The second significant addition is the classification functionality, or predictive modelling. The user will be able to build advanced classifiers at the click of a button, selecting from a range of different models, and will also be able to classify data based on generated models. The classification functionality can be used as an alternative way to finding the most relevant variables to explain a condition, and be used for classification of samples in a diagnostic context.
The presentation methods range from an innovative use of principal component analysis (PCA) to interactive heat maps and flexible scatter plots. All user action is at most two mouse clicks away.