Can the pharmaceutical industry reduce attrition rates?

By I. Kola, John Landis. 2004

4,046 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Immunology, Public Health, Environmental and Occupational Health

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Shamim Nemati
2. Vincent Zoete
3. Olivier Michielin
4. David Gfeller
5. M. Hann
6. R. Upton
7. Chao Yu
8. David E. Shaw

In Immunology

1. Vincent Zoete
2. Olivier Michielin
3. David Gfeller
4. M. Hann
5. I. Kapetanovic

In Public Health, Environmental and Occupational Health

1. Vincent Zoete
2. Olivier Michielin
3. David Gfeller
4. M. Hann
5. David E. Shaw
6. David W. Borhani
7. I. Kapetanovic

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, background): “Drug discovery and development is a time-consuming and costly process that usually lasts for 10-17 years, but with as low as around 10% overall probability of success [295].”
• Computational Approaches in Preclinical Studies on Drug Discovery and Development (2020, background): “Pharmacokinetics and toxicity assessments of preclinical drugs are of great value in reducing the failure rate of new chemical entities (NCEs) in clinical trials (Kola and Landis, 2004; Yang et al., 2018b; Ferreira and Andricopulo, 2019).”
• Molecular obesity, potency and other addictions in drug discovery (2011, background): “, attrition) in 1991 to those in 2000 showed a shift to an increase in failure attributed to toxicological reasons [2].”
• Solid Lipid Nanoparticles for Drug Delivery: Pharmacological and Biopharmaceutical Aspects (2020, background): “On the contrary, it is a known fact that when the biopharmaceutical characteristics of drug candidates are addressed in early stages of discovery programs the consequence is an increase in failures due to lack of efficacy and, to a lesser extent, for toxicity concerns (Kola and Landis, 2004).”
• Drug-target interaction prediction: databases, web servers and computational models (2016, background): “Despite many advances in the past decades, drug discovery is still a costly and inefficient process [1–3], with costs for each new molecular entity (NME) estimated at $1.”
• Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. (2008, background): “Toxicity has almost doubled as a cause of drug attrition from year 1991 to 2000 and is one of the main causes of drug failure [70].”
• Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse (2015, neutral): “The vast majority of drug candidates for neurological disorders succeed in mouse model studies but fail in human clinical trials (Kola and Landis, 2004).”