Over the past decade, pull incentives as a solution to the broken antibiotic market have been proposed to entice companies into antibiotic research and development. These incentives would essentially provide a market, and therefore a return on investment for pharmaceutical companies. Almost all of today’s inadequate antibiotic pipeline is provided by biotech and small pharma. All are threatened with loss of investor interest because of the failed marketplace and many are experiencing difficulty in raising funds either from public or private markets. One alternative to providing money to the “evil” pharmaceutical industry via a substantial pull incentive is to create publicly funded non-profit organizations or public-private ventures that would essentially replace the industry in antibiotic research, development and commercialization. Two proponents of this approach are Lord Jim O’Neill (of the O’Neill Commission or Antimicrobial Resistance Review fame) and Ramanan Laxminarayan of the Center for Disease Dynamics, Economics and Policy and of GARDP. Both, clearly, are key thought leaders in the area.
Antibiotic R&D has had a particularly bad year starting with The Medicines Company who abandoned their antibiotic R&D efforts and sold their antibiotic assets to Melinta late last year right after getting approval for vabomere. This year both Sanofi and Novartis abandoned their antibiotic R&D efforts and divested their clinical and preclinical assets. Allergan, holder of the North American rights to ceftaroline, dalbavancin and ceftazidime-avibactam, also announced that they would divest their antibiotic assets. I have not heard that they were successful. Achaogen has now undergone two efforts at “restructuring” involving virtually eliminating all R&D and has essentially put up the “for sale” sign just after achieving approval for plazomicin. Finally, Melinta abandoned their antibiotic R&D efforts in the face of miserable sales of their recently launched antibiotics including delafloxacin and vabomere.
What is a super-platform?
A “super-platform” is a term which describes a relatively new phenomenon in a modern technological world -- an online-to-offline (O2O) type of digital infrastructure, which spans across multiple sectors of economic activity providing a way for users (both businesses and consumers) to operate with multiple resources, products and services within a uniform, standardized, and highly interconnected way.
Imagine, you want to be able to search for information, shop online, pay for products and services, communicate with someone by email, or chat, create and manage text and spreadsheet documents, translate them into any language on the go, store and organize data like photos and videos, find local restaurants and get driving directions, or just entertain yourself by playing games -- and you prefer to have all of that in one place without needing to search for numerous websites and resources? You can do just that in your single Google account. This is what a super-platform does: it provides a way to conveniently engage in totally different types of activity across different sectors.
The first biologics drug, humanized insulin (5.8 kDa), became available in 1982 following the advent of biotechnology, and it marked a new era in pharmaceutical industry. Modern advances in biotechnology permit large-scale syntheses of biologics in a more or less cost-effective manner. Having once started with large peptides and recombinant proteins, biologics nowadays include a wide range of other entities, such as antibodies, monoclonal antibodies, and more recently, nanobodies and related objects, soluble receptors, recombinant DNA, antibody-drug conjugates (ADCs), fusion proteins, immunotherapeutics, and synthetic vaccines.
(Edited version of this post originally appeared in Forbes)
“It is not the strongest of the species that survives, nor the most intelligent, but the one most adaptable to change” -- Leon C. Megginson
Last year brought about new hope and even more hype around the idea of applying artificial intelligence (AI) for “revolutionizing” drug discovery research -- via machines being able to “learn” chemistry and biology from vast amounts of experimental data to propose potent drug candidates, accurately predict their properties and possible toxicity risks. It is supposed to dramatically minimize failures in clinical trials -- saving R&D budgets, time, and most importantly, lives of patients.