Company decision makers, regulators and companies that acquire promising drugs want to know not only what the drug does, but also how good the data are supporting these claims. Designing a new molecule for a target, or performing pharmacological studies for compound selection, is tough enough without having to become an expert in mathematical and statistical modelling as well to ensure the right experimental design and data interpretation to satisfy picky company heads, experienced regulators or savvy investors.

The problem is, to get the full range of modelling services (i.e. population PK/PD, quantitative systems pharmacology, preclinical and clinical statistics) you need to support a project throughout drug discovery and development, you often have to work with large consulting firms that either torture your data to fit their over-simplified, preconceived "generic" models so that their junior modellers can turn the handle to deliver half a dozen projects or more a year without spending any time thinking your problem through, or confuse you with unnecessarily large (often sold as “mechanistic”) models built on shaky data from unverifiable studies reported over a few decades so that they can get renewed contract, let alone these would cost you an arm and a leg, and you cannot expect any useful insights from these models to help you design the right experiments to get to key answers quickly.

No more.

Since 2011, I have been generating compelling experimental evidence by developing, validating and improving predictive models for over a dozen oncology drug projects at different stages at leading pharmaceutical companies. My work has generated the much-needed clarity for prioritising compounds, experiments and projects for different levels of decision making, and it has been recognised by the Oncology iMed Innovation and Achievement Award at AstraZeneca (from Head of Oncology innovative Medicines & Early Development Susan Galbraith) and as numerous breakthroughs by the Research Portfolio Committee (internal governance board) at Boehringer Ingelheim.

I am a trusted advisor on mathematical and statistical modelling who has collaborated with over 100 chemists, biologists, pharmacologists and statisticians within big pharmas. I created, led and grew the Oncology PK/PD modelling lab at BI in recent years.

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If you are exploring a new therapeutic concept and defining PoM, PoP and PoC criteria, or if you are advancing your drug project to the IND stage to trigger clinical studies, I would like to hear from you.

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You Tao*,  PhD, MSc, BE 

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* This is a Chinese name. Please address me by my first name Tao.