Thread: advice for preclinical biotechs on their in vivo datasets. For preclinical biotechs seeking funding, the difference between a vaguely interesting data set & a fundable data set often comes down to 1) controls used (especially positive controls); 2) dose range tested. 1/7
...We see so many companies fail to utilize obvious positive controls (which is to say, ones that provide some way to contextualize the data with the co’s molecule vs. other experiments done in the field). Why is this? Maybe to save money or time? Or perhaps because it’s...2/7
...viewed as sufficient to show some signal, and risky to have a comparator? Producing data at only a single dose (especially a really high one) is also in many ways a waste of the experiment. It’s critical to design your experiments with these things in mind, even if... 3/7
...you’re on a tight budget. One well-designed experiment in a single oncology model, for instance, is usu. better than two poorly-designed experiments in two different cell lines. Also, it may be more advantageous to spend your capital building out a broader data set for...4/7
...a lead program (e.g., in cancer, multiple xenografts and perhaps a panel of PDX’s) than to generate a smattering of data across several programs. Most of the time, investors will need to feel that the lead program is very compelling to invest (with other pipeline...5/7
...programs potentially adding to the valuation they’ll pay – IF they like your lead). There are also some indications for which investors are unlikely to fund development programs (“graveyards”). It’s important to know if you’re producing data that would support dev't...6/7
...in an indication likely to be of interest to investors. If you’re not sure what investors will require for a seed or A, cultivate some relationships and ask them directly. 7/7
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