The Cost of Drug Development

DiMasi, Joseph A.; Grabowski, Henry G.; Hansen, Ronald W.

doi:10.1056/nejmc1504317

Cited by 110 publications

(62 citation statements)

References 2 publications

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“…Developing a new drug is however slow and expensive. The average cost to bring a new drug to market is > 2.5 billion US dollars (DiMasi et al 2015), which means that tropical diseases such as malaria, schistosomiasis, Chagas' disease, etc., which kill millions of people and infect hundreds of millions of others are 'neglected' (Ioset and Chang 2011;Leslie and Inouye 2011) and that 'orphan' diseases (i.e. those with few sufferers) remain untreatable (Braun et al 2010).…”

Section: Quantitative Structure Activity Relationship (Qsar) Learningmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

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We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem Learn (2018) 107:285-311 is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

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Section: Quantitative Structure Activity Relationship (Qsar) Learningmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

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“…1 In conjunction with an overall ≈90% failure rate of drugs, 2 which impacts directly the rising cost of drug development, this is creating a perfect storm that is threatening to make the development of new drug therapies for CHD prohibitive (estimated currently at $2.6 billion/drug). 3 One potential solution is to use therapeutic targets that have support from genetic studies, such as Mendelian randomization (MR; Figure 1), which is estimated to double the probability that a drug target will succeed in phase III clinical trials.…”

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confidence: 99%

Deciphering the Causal Role of sPLA2s and Lp-PLA2 in Coronary Heart Disease

Talmud

Holmes

2015

ATVB

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Abstract-Over the last 10 to 15 years, animal and human observational studies have identified elevated levels of both proinflammatory secretory phospholipase A2-IIA and lipoprotein-associated phospholipase A2 as potential risk factors for coronary heart disease. However, Mendelian randomization, a genetic tool to test causality of a biomarker, and phase III randomized controlled trials of inhibitors of theses enzymes (varespladib and darapladib) converged to indicate that elevated levels are unlikely to be themselves causal of coronary heart disease and that inhibition had little or no clinical utility. The concordance of findings from Mendelian randomization and clinical trials suggests that for these 2 drugs, and for other novel biomarkers in future, validation of potential therapeutic targets by genetic studies (such as Mendelian randomization) before embarking on costly phase III randomized controlled trials could increase efficiency and offset the high risk of drug development, thereby facilitating discovery of new therapeutics and mitigating against the exuberant costs of drug development. Arterioscler Thromb Vasc BiolNovember 2015 , all of which have been reported to be associated with atherothrombotic disease from animal and human observational studies. The predicted efficacy of their inhibition on the development of atherosclerosis and CHD has been tested using the genetic approach of MR (see Figure 1 for a description of MR and comparison to conventional RCT), and this, together with the outcome of the drug trials, is reviewed. Secretory PLA2s and AtherogenesisThe sPLA2 enzymes are the largest group of this family of enzymes, representing around 30% of all known phospholipases. They are small calcium-dependent, disulphide-rich enzymes with molecular weights ranging between ≈14 and 16 kDa, with the exception of sPLA2-III, which has a molecular weight of ≈55 kDa. 10 The reason for the biological requirement for so many sPLA2s may arise because of their specificity for different phospholipid substrates, as well as their cellular and tissue-specific diversity. 13 The purported impact of sPLA2s on inflammatory diseases, such as atherosclerosis and rheumatoid arthritis, stems from their enzymatic action, generating proinflammatory biolipid mediators ( Figure 2B). Of the sPLA2s, sPLA2-IIA, 14 sPLA2-V, 15 and sPLA2-X 13 have been identified in atherosclerotic plaques by immunohistochemistry. The ability of sPLA2s to promote atherosclerosis is suggested to reflect their action on the phospholipid bilayer of low-density lipoprotein (LDL) particles, generating proatherogenic, small dense LDL with 50% less surface phospholipids. 16 This results in a conformational change in the apolipoprotein B on the LDL particle, exposing proteoglycan-binding sites. This leads to the aggregation and retention of the modified lipoprotein by proteoglycans in the intima, where they are prone to oxidation and subsequent uptake by macrophages, generating foam cells. 17 In the intima of the vessel wall, phospholipase hydrolysis conti...

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“…7 The Tufts Center for the Study of Drug Development estimates that the cost of bringing a new drug to market is $2.6 billion, 8 although the methodology that generated this figure has been questioned. 9,10 Others estimate that it falls between $800 million and $1 billion. 8,11 Manufacturers also point out that the high failure rate during clinical trials for efficacy and safety, estimated at ≈85%, requires them to amortize the costs related to the failed therapies across the prices of new, successful products.…”

Section: Brand/biologic Marketmentioning

confidence: 99%

American Heart Association Principles on the Accessibility and Affordability of Drugs and Biologics

Antman¹,

Creager²,

Houser³

et al. 2017

Circulation

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Net US spending on pharmaceuticals reached $309.5 billion in 2015, an 8.5% increase from the year before, and is expected to reach between $370 and $400 billion by 2020. These current and projected levels have raised serious concerns by policy makers, providers, payers, and patient groups that they are unsustainable and threaten the affordability of and accessibility to much-needed therapies for patients. Two trends related to drugs/biologics and generic drugs/biosimilars underlie this overall increase in spending. First, the market entry prices of innovator pharmaceutical products, or brand drugs and biologics, are at levels that some assessments consider unaffordable to the healthcare system. Second, prices for some established generic drugs such as digoxin and captopril have seen sharp and rapid increases. As an evidence-based patient advocacy organization dedicated to improving the cardiovascular health of all Americans, the American Heart Association has a unique role in advocating for treatments, including medicines that are available, affordable, and accessible to patients. This advisory serves to lay out a set of principles that will guide association engagement in pursuit of this goal.

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The Cost of Drug Development

Cited by 110 publications

References 2 publications

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Deciphering the Causal Role of sPLA2s and Lp-PLA2 in Coronary Heart Disease

American Heart Association Principles on the Accessibility and Affordability of Drugs and Biologics

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