Who is Beyond and what do you do?
Affordable and effective novel cancer therapies discovered and developed based on all accessible, relevant data in a timely manner
Develop, validate and improve quantitative methods and tools for accurate experimental design to enable robust decision making for cancer drug programmes
Why the name Beyond?
It comes from an Albert Einstein quote "once we accept our limits, we go beyond them". It is a spirit we embody at work.
The Obstacles We Tackle
There is a lack of benchmark of translational modelling methods in the pharmaceutical industry. For discovery and development of novel cancer treatments, this means vagueness in tumour model data interpretation and poses challenges for robust decision making in drug discovery & development.
With nearly a decade of experience with drug discovery, I have gained deep understanding in cancer treatments (radiation therapy, chemotherapy, targeted therapy, cancer immunotherapy) and DMPK (human PK prediction for small and large molecules, PBPK), I have helped pharmaceutical clients successfully advance their drug discovery projects into the clinics.
Here are some common questions I help clearly answer.
Clinical -> preclinical translation:
What degree of preclinical efficacy is necessary to outcompete in direct (same target same indication) and indirect (different mode of action same indication) competitions?
What is the necessary target compound profile (e.g. target engagement IC50, cellular growth inhibition GI50, and pharmacokinetics profile) to outcompete in direct and indirect competitions? How to design a make-or-break experiment to test it?
Which combination treatment partner is the best for the candidate treatment?
How to predict clinical efficacy (e.g. ORR) for the proposed mono-/combi-treatment?
Preclinical -> clinical translation:
How reproducible are preclinical tumour models?
Does variability in a tumour model from both in-house and published sources change the Exposure—Target Engagement—Disease Modulation/Physiological Response—Outcome relationship? Does a preclinical model over- or under-predict clinical efficacy?
Based on the preclinical proof of mechanism study, what is the required degree of target engagement to provide the necessary disease modulation in the clinical proof of concept study?
What is the best clinical dosing schedule for an optimal therapeutic window?