Financial returns on R&D: looking back at history, looking forward to adaptive licensing

The political economics of cancer drug discovery and pricing

Drug discoveries can, when used appropriately, save lives. Since 1970, cancer death rates among people aged under 65 have halved in countries such as the USA and the UK. Despite pharmaceutical market imperfections and fears about the prices of new treatments, further progress should be possible during the 2020s. Anticancer medicine outlays account for 0.1-0.2% of the gross domestic product (GDP) of developed countries. Total cancer service spending typically stands at ∼0.8% of GDP. The affordability of these sums is a political calculation. Improvements in the efficiency of drug development and global access to effective therapies are desirable. However, from a public interest perspective, these goals should not be pursued in ways that understate the value of better treatment outcomes and threaten the funding available for ongoing innovation.

Assembling the Puzzle of Taxifolin Polymorphism

A large amount of the current literature dedicated to solid states of active pharmaceutical ingredients (APIs) pays special attention to polymorphism of flavonoids. Taxifolin (also known as dihydroquercetin) is an example of a typical flavonoid. Some new forms of taxifolin have been reported previously, however it is still unclear whether they represent polymorphic modifications. In this paper, we tried to answer the question about the taxifolin polymorphism. Taxifolin microtubes and taxifolin microspheres were synthesized from raw taxifolin API using several methods of crystal engineering. All forms were described with the help of spectral methods, scanning electron microscopy (SEM), X-ray powder diffraction (XRPD), and thermal analysis (TA). SEM reveals that the morphology of the solid phase is very specific for each sample. Although XRPD patterns of raw taxifolin and microtubes look similar, their TA profiles differ significantly. At the same time, raw taxifolin and microspheres have nearly identical thermograms, while XRPD shows that the former is a crystalline and the latter is an amorphous substance. Only the use of complex analyses allowed us to put the puzzle together and to confirm the polymorphism of taxifolin. This article demonstrates that taxifolin microtubes are a pseudopolymorphic modification of raw taxifolin.

Repositioning of fluoroquinolones from antibiotic to anti-cancer agents: An underestimated truth

Increasing development costs and higher failure rate in clinical trials has reduced the repertoire of newer drugs in the market for clinical use. The most appropriate approach to end the search for newer drugs is "Repositioning", as it requires less time and money to explore new indication of existing drug or failed drug. In the past, several drugs have been repositioned for different indication but the full potential remains unharnessed. With rise in cancer prevalence and treatment costs, it is imperative to search for newer drugs and the use of repositioning approach may help us. Fluoroquinolones has been used as antibiotics for over four decades now, but recent research highlighted their use as pharmacological compounds with multifaceted implication. Repositioning of fluoroquinolones into anti-cancer molecule seems to be a highly plausible option owing to their profound immunomodulatory, pro-apoptotic, anti-proliferative and anti-metastatic potential. The present review provides a comprehensive account of the recent and past explorations pertaining to the anti-cancer activity of fluoroquinolones and also discusses the various approaches that are being considered to remodel them for the treatment of cancer.

When Quality Beats Quantity: Decision Theory, Drug Discovery, and the Reproducibility Crisis

A striking contrast runs through the last 60 years of biopharmaceutical discovery, research, and development. Huge scientific and technological gains should have increased the quality of academic science and raised industrial R&D efficiency. However, academia faces a "reproducibility crisis"; inflation-adjusted industrial R&D costs per novel drug increased nearly 100 fold between 1950 and 2010; and drugs are more likely to fail in clinical development today than in the 1970s. The contrast is explicable only if powerful headwinds reversed the gains and/or if many "gains" have proved illusory. However, discussions of reproducibility and R&D productivity rarely address this point explicitly. The main objectives of the primary research in this paper are: (a) to provide quantitatively and historically plausible explanations of the contrast; and (b) identify factors to which R&D efficiency is sensitive. We present a quantitative decision-theoretic model of the R&D process. The model represents therapeutic candidates (e.g., putative drug targets, molecules in a screening library, etc.) within a "measurement space", with candidates' positions determined by their performance on a variety of assays (e.g., binding affinity, toxicity, in vivo efficacy, etc.) whose results correlate to a greater or lesser degree. We apply decision rules to segment the space, and assess the probability of correct R&D decisions. We find that when searching for rare positives (e.g., candidates that will successfully complete clinical development), changes in the predictive validity of screening and disease models that many people working in drug discovery would regard as small and/or unknowable (i.e., an 0.1 absolute change in correlation coefficient between model output and clinical outcomes in man) can offset large (e.g., 10 fold, even 100 fold) changes in models' brute-force efficiency. We also show how validity and reproducibility correlate across a population of simulated screening and disease models. We hypothesize that screening and disease models with high predictive validity are more likely to yield good answers and good treatments, so tend to render themselves and their diseases academically and commercially redundant. Perhaps there has also been too much enthusiasm for reductionist molecular models which have insufficient predictive validity. Thus we hypothesize that the average predictive validity of the stock of academically and industrially "interesting" screening and disease models has declined over time, with even small falls able to offset large gains in scientific knowledge and brute-force efficiency. The rate of creation of valid screening and disease models may be the major constraint on R&D productivity.