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    Home > Medical News > Medical World News > With low clinical trial success rate and low use efficiency, how to break through the bottleneck of "double low" new drug research and development?

    With low clinical trial success rate and low use efficiency, how to break through the bottleneck of "double low" new drug research and development?

    • Last Update: 2022-09-30
    • Source: Internet
    • Author: User
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    【Pharmaceutical Network Market Analysis】In recent years, due to multiple resonances such as policies, capital, and talents, domestic new drug research and development has been hot, promoting the rapid development of
    China's innovative drug industry.

    According to data, the scale of China's innovative drug market in 2020 has reached 140 billion US dollars, of which 21 domestic new drugs have been approved for listing, and the number is almost the same as that of imported new drugs, reaching a record high
    .

    However, the industry pointed out that the research and development of innovative drugs still faces the bottleneck of "double low", that is, the success rate of drug clinical trials is low, the clinical use of drugs is inefficient, and the bottleneck needs to be broken
    .

    New drug research and development is facing a "double low" bottleneck, how to break through? (Image source: pharmaceutical network) New drug research and development is facing a "double low" bottleneck In terms of drug clinical trials, with the continuous increase in R&D investment of domestic pharmaceutical companies, although there are many candidates for clinical trials, the proportion of candidates who can survive to successful approval for listing is not large
    .

    Previously, there have been many cases
    of drug clinical trial failure.

    For example, there have recently been media reports that the international phase III clinical trial of Green Valley Pharmaceutical's Alzheimer's disease (AD) drug GV-971 (ganlute sodium capsule, trade name: Phase 9 Phase I) has been stopped
    .

    As for the reason for the early termination, the company said that the main reason for the initiated financing was that it was not immediately in place due to the impact of the cold winter of
    the pharmaceutical capital market.

    At the same time, based on the characteristics and needs of subjective cognitive function assessment in Alzheimer's disease patients, and the epidemic situation caused more enrolled subjects to be unable to participate in offline visits on a regular basis, the drop-off rate showed an increasing trend, and the quality risk of clinical research and the cost of research gradually increased
    .

    Kanghong Pharmaceutical Division replied to investors on the interactive platform in May this year that on April 9, 2021, the company stopped the international phase III clinical trial
    of Compasip Ophthalmic Injection based on the recommendations of the Scientific Steering Committee and the company considered a number of factors.

    New drug research and development often has the characteristics of high research and development investment, long research and development cycle, from the perspective of drugs that have failed in research and development in recent years, there is no shortage of cases of clinical failure during the III period, and once the failure is declared, it means that the investment in the new drug research and development project is adrift, and the loss of pharmaceutical companies may be quite heavy
    .

    In terms of the efficiency of clinical use of drugs, although many approved drugs have emerged in the domestic market, most of the patients who use drugs cannot benefit
    from them.

    Taking the PD-1 that has been developed in recent years as an example, there are currently 8 products on the market, of which 7 have entered medical insurance, and there are still a large number of competitors on the track to declare listing
    .

    The industry said that the role of PD-1 therapy in different tumors and different populations varies greatly, and some patients may achieve long-term survival, but for other patients, the use of tumor immune drugs is even less effective than targeted therapy
    .

    Therefore, the phenomenon of blind research and development on a track should be avoided, and some valuable explorations can be made according to the actual needs of patients to improve the efficiency
    of clinical use of drugs.

    How to break through? In view of the "double low" problem of new drug research and development, the industry believes that we should pay attention to the development and clinical application of new drugs by basic research, do a good job in the transformation from "science" to "technology", and attach importance to production, education and research, accelerate the transformation of scientific research results from samples to products to commodities, and apply scientific and technological achievements to health undertakings
    .

    Among them, in the preclinical stage, many industry insiders mentioned that they can use high-quality big data and artificial intelligence to tap reliable biomarkers
    related to disease prevention, diagnosis and treatment.

    The artificial intelligence + new drug research and development model has been placed high hopes
    by the industry.

    The process of new drug development involves a large amount of data, including literature, compound data, target data, patent data, clinical trial data, real-world data, drug review and approval data, market sales data, etc
    .

    AI technology takes data as the core and breaks the traditional research and development model, which is considered by the industry to be an important driving force for improving the efficiency of new drug research and development, and will achieve cost reduction and efficiency
    improvement of new drug research and development.

    At present, many domestic pharmaceutical companies have entered the artificial intelligence + new drug research and development track, and the industry has entered a stage of rapid growth, but as an emerging field, it also faces some challenges, such as the challenges brought about by the complexity of biology, and the problems of China's pharmaceutical big data such as less data, incomplete data, and inconsistent data standards that need to be further cracked
    .

    Disclaimer: Under no circumstances does the information herein or the opinions expressed in this article constitute investment advice
    to any person.

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