Tiresias: Exceeds Expectations

"So top grade’s ‘O’ for ‘Outstanding’," Hermione was saying, "and there's A – ".

“No, ‘E’," George corrected her, " ’E’ for ‘Exceeds expectations’. "

J.K. Rowling

My friends,

Tiresias would like to congratulate the Chairman and Board of Optiscan for the announced acquisition of the Intellectual Property (IP) Dataset of 228 patients’ clinical, Optiscan confocal laser digital biopsies, and corresponding conventional histopathological micrography, and clinical follow up data: a veritable encyclopaedia of almost immeasurable value to Optiscan at this juncture in its growth.

Tiresias notes some doubting Thomases amongst his friends, questioning this acquisition. Tiresias has no doubts. Tiresias would like to assure all his friends just how valuable this IP really is for Optiscan. The first and simplest way to address this is to consider what might this cost Optiscan if they were to try to do this now. That Optiscan needs this IP now, to support further regulatory approval and sales, Tiresias has no doubt. The costing is relatively simple. Tiresias could give his estimate, for he has participated in medical trials, but he will not do this, though he is confident that he would have a very close actual figure. Tiresias will instead use a cost figure offered to Optiscan, for such a study by the leading cancer hospital, and as so happens, one which Tiresias had had a job offer at and had accepted, way back when he was working in America, but for reasons otherwise he chose to return to Australia. The offer was for Optiscan to pay $US 20,000. Setting aside the fact that Tiresias is certain that the study, albeit done at a reputable centre, would not have been as comprehensive or as detailed as the IP Dataset acquired, it would be roughly at $AUD30,000.00 per patient. This would make the present Dataset cost Optiscan $6,840,000. Yes, my friends over $6 million Australian. But as they say, that’s not all. First, Tiresias would like to point out Professor Farah, to Tiresias’s knowledge, has unique qualifications, perfectly suited for Optiscan’s current needs. Being an oral physician and a histopathologist he is able to produce the clinical, CLE in-vivo real-time digital histopathological, and then the histopathological micrographs, all correlated with initial and follow-up clinical and histopathological correlation of all cases. This is something even Tiresias’s American alma mater, and his would-be alma mater, could not have done.

But then there are other factors to consider. First, think about the time this would've taken if Optiscan were to start such a study now. To even get regulatory and ethical approvals for the study would've taken minimum of one year and at least half a million dollars before even starting, and then anywhere between two and three years if not more, to complete, a year to analyse, and with no guarantee of quality. Just the saving in time justifies the minuscule share issue, which by the way is escrowed, shows the confidence of our MD, in the data and the path forward for Optiscan, for he has very much put all his skin in with us into this project

The second point that Tiresias would make is that in acquiring this IP Dataset, to comply with ASX listing rules the board would've had to have an independent valuation of the IP. Tiresias expects we will know this independent valuation, and if done competently, Tiresias would be surprised if it wasn’t close to Tiresias’s valuation, which is actually an undervaluation. But why is it that the cost is an undervaluation. Because the cost does not reflect the real value to Optiscan. This is an arbitrage. Remember, Bill Gates bought DOS for $US50,00! For Optiscan this is critical invaluable high-quality data which will be used not just on oral and head and neck cancer but for all other areas of applications. It points the way for everything else. Furthermore, with the correlation and such a large dataset and such clinical digital and conventional histopathological examinations of normal tissue, pre-cancer, and cancer, this will form the basis for revolution in pathology by training machine learning/AI; the new wave in pathology.