Innovation in the Pharmaceutical Industry

The pharmaceutical industry is, similar to the chemical industry, primarily based on physical products, a fact that shapes the general product innovation process. Drugs interact with the human organism, a system with multiple coupled parameters. This coupling is an immutable natural fact leading often to an uncertain outcome even if only one single parameter is altered.

Furthermore, due to the coupling of these parameters, modularization of development at the basic chemical level often is not feasible, by that increasing technical risks of innovation as mentioned above. An example for small changes with large impact is given in the following figure.

Structure-activity relationship of cortisol derivatives
Structure-activity relationship in drugs
Mineralocorticoid receptor activity of Fludrocortisone (9α-Fluorocortisol) 200-400 times higher than cortisol.[1]

In view of the tremendous effect of minute molecular differences, technological risks in pharmaceutical development are addressed by the strict legal framework for new product development. Starting from basic research, primarily conducted by academic institutions and heavily funded by the public sector,[2] this framework leads subsequently to a mainly privately funded development with high standardisation of technical risk management in the pharmaceutical industry. The basic steps common to all drugs based on a new molecular entity (NME) are depicted in the figure below. Transition probabilities between the individual phases and the resulting overall probability of success are rather low in the pharmaceutical industry. In addition to the mandatory studies from clinical phase I-III, increasing importance is attributed to phase IV post-marketing studies. surveillance and pharmacovigilance studies which aim to accumulate longer-term data on safety, efficacy and potential new indications from large numbers of subjects under everyday conditions.[3]

Overview of legal standard process of drug development
Drug development process
Sources: Adapted from Girotra et al., estimated phase transition probabilities from DiMasi et al.[4]

The approach comprises following steps:

  • Basic research covers compound discovery e.g., by computer-aided design as well as massive parallel screening of substances. Large substance libraries containing thousands of substances, or more are synthesised and tested for moderation of certain enzyme activity, association to proteins, influence on cell activity, cellular toxicity etc. Additionally, more recently biological testing is supplemented by in-silico screening using machine learning.[5]
  • In pre-clinical research model organisms are used to investigate safety and metabolism of promising drug candidate from basic research. Usual model organisms are mice, rats, New Zealand rabbits, and other mammals including primates at later stages.
  • Clinical Phase I addresses drug safety with trials of 20-100 healthy individuals or people with the disease, depending on indication. The main purpose is to determine drug safety and dosage.
  • Clinical Phase II comprises clinical studies with up to several hundred patients suffering from the disease/condition addressed by the drug. The main focus is efficacy of drug treatment and side effects of the drug.
  • Clinical Phase III studies investigate efficacy of the drug in detail and monitors adverse reactions of patients. These studies have participant number of approximately 300-3000 patients.
  • The drug agency review is initiated by a new drug application from e.g., a pharmaceutical manufacturer. All data from preclinical tests to Clinical Phase III have to be submitted for review. Market approval for the drug can be denied if statistics of clinical trials e.g., on efficacy or side effects are not convincing.

Market launch is only permitted after successful approval at the end of the drug agency review. The corresponding probabilities shown in the figure above add up to an overall probability of success of less than twelve percent from entering clinical phase I until market approval. Despite better testing systems this percentage has even decreased compared to prior investigations. In addition to that, failure rates in basic and per-clinical research are even higher.

Challenges and Outcome of Drug Development

While drug development has already been known to be notoriously difficult and time consuming,[6] it has become even more challenging due to better performance of drugs already marketed as well as increasing regulatory and safety demands for new drugs.[7] While in agreement with these challenges reports exist on increasing drug development costs and a decline in R&D efficiency over several decades,[8] more recently the efficiency in pharma development seems to have recovered and drug development cost seem to have diminished.[9] Furthermore, a recent literature  showed a considerable variation for drug development costs between different literature sources depending on study method, interest rates used and other factors.[10] In addition to that, probabilities of development success also differ depending on the disease targeted and the type of drug as do the respective development costs.[11] Still the decade-long decline in pharmaceutical R&D efficiency lead to a growing concern about the profitability of market introduction of new drugs originating from longer development times and shorter market exclusivity for companies developing such drugs.[12]

On average, the development of a drug containing a new active ingredient or new molecular entity takes more than ten years. This estimation is based on the delay between the filing date of a first patent application (patent priority) and the market approval of the respective drug as disclosed by the Food and Drug Administration.

Distributions of different delays between inventions and drug market approvalDistributions of different delays between inventions and drug market approval
Sources: Own elaboration based on the “Orange Book” and patent data by the United States Patent Office[13]

Please notice, that inventions themselves as well as compiling the filing documents for the patent authority often involve prior significant work so that the start of the development process lies earlier and the durations disclosed are the bare minimum of time passed. Furthermore, this analysis only applies to drugs with patent protection or, in other words, drugs the development of which entailed inventions of sufficient novelty for patenting. Non-substance related novelties like new formulations, application forms or usage of an existing drug for additional indications take shorter from first invention until market introduction, as clearly visible from the different average durations (3708 days for substance-related inventions versus 2414 days for non-substance-related inventions).

Pharma companies employed several strategies to cope with increasing drug development costs. Some simply increased the prices of their drugs already marketed or demanded unwarranted high prices for new drugs.[14] Another avenue of easing development is opened by indication extensions, using the fact that one drug can often be used for several indications. This frequently employed strategy increases the overall probability of success due to already known toxicity profile and side effects.[15] In addition to that, most of the launched drugs do not contain a completely new molecular entity, resulting in a significantly lower probability of failure.[16]

Apart from the strategies employed by the pharma companies researches suggested several ways to reverse the trend of declining R&D efficiency. One suggestion was to shift resources from not-promising drug candidates in late clinical research towards candidates in early non-clinical research, supplemented by collaborative research and outsourcing, use of biomarkers as patient identifiers to improve probability of success in clinical trials, reduction of time span for each development step by appropriate target and portfolio selection as well as improved project management[17] The use of biomarkers is backed up by the finding that usage of biomarkers increased the probability of success of clinical studies between 2005 and 2015 in all fields but metabolic/endocrinological indications.[18] Another suggestion to reduce development costs is to increase the respective firm’s efforts to learn from past failures and to terminate non-promising projects at an early stage and to avoid costly unnecessary further development.[19] Other means suggested to improve development efficiency are exploitation of genomics and proteomics, repurposing, and repositioning of existing drug molecules, collaborative research, targeting under-served therapeutic fields, outsourcing strategies and pharmaceutical modelling and artificial intelligence as potential approaches to enhance performance. [20] From a technical point of view, especially big data approaches have raised increased interest.[21] Leveraging known molecular structures for new drug development has been identified by several researchers as one promising approach for lead structure development.[22] Not surprisingly to anyone skilled in the art of pharmaceutical development, a lack of understanding of disease biology was identified as main obstacle for better predictions of clinical success, which lead to the suggestion to follow a different approach using data science based on a clear strategic vision as way forward.[23] Unfortunately, there is no good externally available measure for adoption of such processual improvements yet.

Low but increasing economic risks in the pharmaceutical industry are generally connected to reimbursement especially for new prescription drugs or therapy forms like precision medicine.[24] While this was less critical in the past, increasing scrutiny of public health insurances on efficacy of new drugs compared to established treatments led to formation of whole departments focusing on reimbursement issues within the researching pharmaceutical corporations. Drugs and therapies are compared to the best currently available treatment, a factor providing an additional challenge increasingly difficult to overcome.[25] For drugs with little benefit pharma companies resort to increasing the number of participants in clinical studies to show statistical significance. However, this method of ensuring reimbursement results in the highest clinical study cost necessary for the drugs with the lowest medical benefit.[26]

Footnotes

  1. Beck, K. R., Thompson, G. R., & Odermatt, A. (2020). Drug-induced endocrine blood pressure elevation. Pharmacological Research, 104311. https://doi.org/10.1016/j.phrs.2019.104311.
    Grossmann, C., Scholz, T., Rochel, M., Bumke-Vogt, C., Oelkers, W., Pfeiffer, A. F. H., Diederich, S., & Bähr, V. (2004). Transactivation via the human glucocorticoid and mineralocorticoid receptor by therapeutically used steroids in CV-1 cells: a comparison of their glucocorticoid and mineralocorticoid properties. European Journal of Endocrinology, 151, 397–406. https://doi.org/10.1530/eje.0.1510397.
    Rupprecht, R., Arriza, J. L., Spengler, D., Reul, J. M. H. M., Evans, R. M., Holsboer, F., & Damm, K. (1993). Transactivation and synergistic properties of the mineralocorticoid receptor: relationship to the glucocorticoid receptor. Molecular Endocrinology, 7, 597–603. https://doi.org/10.1210/mend.7.4.8388999.
  2. Chakravarthy, R., Cotter, K., DiMasi, J., Milne, C.-P., & Wendel, N. (2016). Public- and Private-Sector Contributions to the Research and Development of the Most Transformational Drugs in the Past 25 Years: From Theory to Therapy. Therapeutic Innovation & Regulatory Science, 50, 759–768. https://doi.org/10.1177/2168479016648730
  3. Grudzinskas, C. (2007). Design of Clinical Development Programs. In A. J. Atkinson, D. R. Abernethy, C. E. Daniels, R. L. Dedrick, & S. P. Markey (Eds.), Principles of Clinical Pharmacology (2 ed., pp. 501–517). Academic Press. https://doi.org/10.1016/B978-012369417-1/50073-0.
    Suvarna, V. (2010). Phase IV of Drug Development. Perspectives in Clinical Research, 1, 57–60. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148611/.
  4. Girotra, K., Terwiesch, C., & Ulrich, K. T. (2007). Valuing R&D Projects in a Portfolio: Evidence from the Pharmaceutical Industry. Management Science, 53, 1452–1466. https://doi.org/10.1287/mnsc.1070.0703.
    DiMasi, J. A., Grabowski, H. G., & Hansen, R. W. (2016). Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics, 47, 20–33. https://doi.org/10.1016/j.jhealeco.2016.01.012.
  5. Follmann, M., Briem, H., Steinmeyer, A., Hillisch, A., Schmitt, M. H., Haning, H., & Meier, H. (2019). An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer — against all odds? Drug Discovery Today, 24, 668–672. https://doi.org/10.1016/j.drudis.2018.12.003.
    Suvarna, V. (2010). Phase IV of Drug Development. Perspectives in Clinical Research, 1(2), 57–60. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3148611/.
    van Vleet, T. R., Liguori, M. J., Lynch, J. J., Rao, M., & Warder, S. (2019). Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals. SLAS DISCOVERY: Advancing the Science of Drug Discovery, 24, 1–24. https://doi.org/10.1177/2472555218799713.
  6. Scherer, F. M. (2010). Chapter 12 – Pharmaceutical Innovation. In B. H. Hall & N. Rosenberg (Eds.), Handbook of The Economics of Innovation, Vol. 1, pp. 539–574. https://doi.org/10.1016/S0169-7218(10)01012-9
  7. Kiriiri, G. K., Njogu, P. M., & Mwangi, A. N. (2020). Exploring different approaches to improve the success of drug discovery and development projects: a review. Future Journal of Pharmaceutical Sciences, 6, 27. https://doi.org/10.1186/s43094-020-00047-9
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  9. Riedel, N. H. (2021). Impact of Strategic Changes on R&D Efficiency in Drug Development. In ISPIM Conference Proceedings (No. 2617200842, pp. 1–18). The International Society for Professional Innovation Management (ISPIM).
  10. Rennane, S., Baker, L., & Mulcahy, A. (2021). Estimating the Cost of Industry Investment in Drug Research and Development: A Review of Methods and Results. INQUIRY: The Journal of Health Care Organization, Provision, and Financing,
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    Schlander, M., Hernandez-Villafuerte, K., Cheng, C.-Y., Mestre-Ferrandiz, J., & Baumann, M. (2021). How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment. PharmacoEconomics, 39, 1243–1269. https://doi.org/10.1007/s40273-021-01065-y.
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    Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics (Oxford, England). 20(29394327), 273–286. https://doi.org/10.1093/biostatistics/kxx069.
    Schlander, M., Hernandez-Villafuerte, K., Cheng, C.-Y., Mestre-Ferrandiz, J., & Baumann, M. (2021). How Much Does It Cost to Research and Develop a New Drug? A Systematic Review and Assessment. PharmacoEconomics, 39, 1243–1269. https://doi.org/10.1007/s40273-021-01065-y.
  12. Berndt, E. R., Nass, D., Kleinrock, M., & Aitken, M. (2015). Decline In Economic Returns From New Drugs Raises Questions About Sustaining Innovations. Health Affairs, 34(2), 245–252. https://doi.org/10.1377/hlthaff.2014.1029.
    Lietzan, E. (2018). The Drug Innovation Paradox. Missouri Law Review, 83(1), 6. https://scholarship.law.missouri.edu/mlr/vol83/iss1/6.
  13. Data retrieved 15 August 2024, Approved Drug Products with Therapeutic Equivalence Evaluations | Orange Book available at https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic-equivalence-evaluations-orange-book, patent data at the PatentsView website.
  14. Herper, M. (2017). Why Did That Drug Price Increase 6,000%? It’s The Law. Forbes. https://www.forbes.com/sites/matthewherper/2017/02/10/a-6000-price-hike-should-give-drug-companies-a-disgusting-sense-of-deja-vu.
  15. Sahragardjoonegani, B., Beall, R. F., Kesselheim, A. S., & Hollis, A. (2021). Repurposing existing drugs for new uses: a cohort study of the frequency of FDA-granted new indication exclusivities since 1997. Journal of Pharmaceutical Policy and Practice, 14, 3. https://doi.org/10.1186/s40545-020-00282-8
  16. Darrow, J. J., Avorn, J., & Kesselheim, A. S. (2020). FDA Approval and Regulation of Pharmaceuticals, 1983-2018. JAMA, 323(2), 164–176. https://doi.org/10.1001/jama.2019.20288.
  17. Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery, 9(3), 203–214. https://doi.org/10.1038/nrd3078.
  18. Wong, C. H., Siah, K. W., & Lo, A. W. (2019). Estimation of clinical trial success rates and related parameters. Biostatistics (Oxford, England). 20(29394327), 273–286. https://doi.org/10.1093/biostatistics/kxx069.
  19. Garzón-Vico, A., Rosier, J., Gibbons, P., & McNamara, P. (2020). The Impact of Failure and Success Experience on Drug Development. Journal of Product Innovation Management, 37(1), 74–96. https://doi.org/10.1111/jpim.12514.
  20. Kiriiri, G. K., Njogu, P. M., & Mwangi, A. N. (2020). Exploring different approaches to improve the success of drug discovery and development projects: a review. Future Journal of Pharmaceutical Sciences, 6(1), 27. https://doi.org/10.1186/s43094-020-00047-9.
    Hingorani, A. D., Kuan, V., Finan, C., Kruger, F. A., Gaulton, A., Chopade, S., Sofat, R., MacAllister, R. J., Overington, J. P., Hemingway, H., Denaxas, S., Prieto, D., & Casas, J. P. (2019). Improving the odds of drug development success through human genomics: modelling study. Scientific Reports, 9(1), 18911. https://doi.org/10.1038/s41598-019-54849-w.
  21. Yildirim, O., Gottwald, M., Schüler, P., & Michel, M. C. (2016). Opportunities and Challenges for Drug Development: Public–Private Partnerships, Adaptive Designs and Big Data. Frontiers in Pharmacology, 7, 461. https://doi.org/10.3389/fphar.2016.00461
  22. Grisoni, F., Merk, D., Consonni, V., Hiss, J. A., Tagliabue, S. G., Todeschini, R., & Schneider, G. (2018). Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity. Communications Chemistry, 1, 44. https://doi.org/10.1038/s42004-018-0043-x.
    Lo, Y.-C., & Torres, J. Z. (2016). Chemical Similarity Networks for Drug Discovery. Special Topics in Drug Discovery. https://doi.org/10.5772/65106.
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