How can we improve the pace of drug development and approval?

Here’s the one-hundred dollar question: how can we improve the pace of drug development and approval? Why does it take so long to develop new drugs and treatments and get them approved?

Here’s our take on this obstacle towards the ideal future. What do you think?

Novel and innovative therapeutics and treatments will need to be developed and approved in order to slow down the aging process. Such therapies, however, tend to take an extremely long time to be developed and approved, which means progress towards the preferred future state will be extremely slow as well.

Drug development process is composed of multiple steps, each of which requires time, effort, and significant monetary investment[22]. Once a certain developmental candidate has been identified in the lab, almost always after clinical trials in animals, it goes through three phases of clinical trials, as follows -


  • Phase 1: the candidate is tested on healthy individuals, to determine its safety. About 63% of all candidates pass this phase successfully.
  • Phase 2: the candidate is tested on a small number of sick patients to assess its efficiency and side effects. About 30% of all candidates move on to the next stage.
  • Phase 3: trials are being conducted on a large number of patients. About 58% of candidates make it through this stage.
  • Phase 4: by this time the drug has been approved for marketing and is regularly used by thousands of volunteers who are undergoing constant surveillance to identify any side effects that weren’t noticed in the smaller trials in previous phases.
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    The specific assessments vary, but the general agreement is that somewhere between 5 to 13.8% of all developmental candidates that enter phase 1 of clinical trials, end up being approved [23] [24]. As for time, all three first phases combined require 6-7 years, followed by another 0.5 - 2 years for the FDA review and for manufacturing to start. Finally, the fourth phase can take 0.5 - 10 years[25].

    Since the pace of new drug development is so slow and fraught with failure, new medications arrive very slowly to the market. Additionally, the high costs involved in the development of every new drug, as well as the need to compensate for past failures, require pharma companies to charge extraordinarily large sums for the medications that make it to the market. As a result, even if successful anti-aging therapeutic were to be developed, it would probably be accompanied by a hefty price tag that would keep the medication out of the hands of many.

    **User Groups
    **This obstacle is relevant to all user groups, since future anti-aging medications may be used at any stage in life. However, at the moment this obstacle is especially relevant for people who are 60 years old or older, and who suffer from at least one chronic disease.

    While the regulator is often held at fault for slowing the pace of progress, at least in this case it is performing admirably well. The FDA only takes eight months to review any new treatment, which is a reasonable amount of time considering that its scientists must go over the results of several clinical trials and produce a 200 page report[27]. The European Medicines Agency (EMA) similarly requires around six months to assess new submissions[28].
    The main difficulty behind this obstacle is that the science underlying age-related diseases (and diseases in general) is complex and is not yet fully understood. The unfortunate implication is that pharma firms cannot forecast in advance whether or not a certain therapeutic candidate will achieve success or not, and what kind of side effects it may carry[26].
    Additionally, there are currently no good models of human tissues and body to test new drugs on in the lab, which may account for the fact that only 30.7% of all candidate drugs successfully pass the second phase, in which they’re tested for their actual efficacy in human beings.

    Is one answer to be found in NOT going after new drugs, but looking at drugs that already exist and have shown results? Is there a case for re-purposing and improving/further developing those existing ‘remedies?’

    @LisaCovington -
    As far as I know, any improvement or further development of an existing drug, requires the upgrade to undergo the same arduous procedure of clinical trials.

    It may be, however, that part of the solution is to combine existing drugs. Not in one pill (which again requires clinical trials) but by MD-given prescription.

    @Assaf_Horowitz, I wonder, with your experience in health care solutions, you have insights here? Are we correct in arguing that the current regime for approving drugs and treatments is too slow? If so, why is that the case – and could it be sped up?

    Thank you @NickOttens,

    There are many reasons why the new drug’s design and approval processes are slow. You can find bottlenecks in each building block of the drug development value chain.

    In my opinion, we need to re-think this process and advocate for embracing innovation and cutting-edge-technologies, especially in diagnosis and drug design know-how.

    Diagnosis is a key factor here since in most of the diseases we are diagnosing late when the patient is already in the symptomatic phase. Let’s take Parkinson (PD) for example - by the time parkinsonism is clinically evident, almost 50% of dopaminergic neurons are already lost, leaving us with poor outcomes of the current treatments. Since the majority of patients participating in clinical trials are in a progress stage of the disease, it is too late for preventive drugs. PD is only one example out of many, and the same goes for Alzheimer’s, Multiple Sclerosis, ALS, etc.,

    We must embrace and support new technologies for early diagnosis, since (obviously) the conventional diagnostic tools and clinical routines are not good enough.

    Regarding drug design - I agree with @LisaCovington that investing in repurposing is a beneficial direction. Although @Roey is right and we still need to validate this drug through clinical trials, it should be a shorter process, since the drug has already made it through phase 1 (safety ).
    In addition, I think we have to promote the implementation of novel tools and new methods coming from the artificial intelligent field. Applying machine / deep learning solutions along with cutting edge bio-technologies (CRISPR and gene editing, stem cells, tissue printing,etc.,) we might better design and test new artificial drugs on artificial tissues.

    It seems that another contributing factor to the slow pace of drug development is lack of funding, and time spent attempting to secure funding, for expensive research processes such as bio-marker testing, culture growth, and clinical trials to name a few. What impact might innovations such as nextgen artificial intelligence and blockchain technologies have on this barrier?

    @LisaCovington - I don’t think lack of funding is an issue. The high dividends from a successful drug ensure that pharma companies still fund the discovery new drugs despite the low chance of success.

    @SamBlake - have you seen @Assaf_Horowitz 's answer? Very interesting!

    @Assaf_Horowitz Thanks for your great insight here! I have a couple of follow-up questions.

    How does the regulatory approval process differ between diagnostics and therapeutics?

    What, in your view, is slowing the progress of early detection technologies despite the rather widespread agreement that they are of substantial importance?

    Finally, as for using ML/DL to improve the drug development process, what do you think are the key barriers to a world where that is the status quo?

    @CarolynPorter, your experience may have given you some unique insights into this topic: the slow pace of drug development and approval. Do you think @Roey’s take is the correct one? And what - if anything - could be changed to overcome this obstacle?

    Here is a link to an article about the challenges and promise of using mice to study longevity. The drug approval process requires efficacy and safety testing in animals. Having a good, well accepted model is important for successful drug development.

    There are several root causes for aging-related degeneration, disease and death, and many angles of approach to preventions, delays and cures. Virtually the entire anti-aging community is united in recognizing that a revolution in healthspan requires broad and deep research into the etiologies of biological aging, painstaking development of prospective therapies, expensive clinical trials, and bold, risky entrepreneurial ventures. To that end, a group of esteemed anti-aging researchers and crusaders in Northern California are uniting in creating a $12B ballot initiative to fund, research, develop, and commercially translate anti-aging treatments, with a priority for preventive measures: the California Healthy Aging Initiative of 2020 (CHAI 2020). Over 1,000,000 signatures are required to get the initiative on the ballot, which is expected to cost ~$6,000,000, followed by advertising expenses, etc. We already have endorsement from several stellar anti-aging thought leaders. This effort promises to be the greatest investment and humanitarian advance of our era. Can the XPRIZE community help? We are not ready to accept donations yet, but people who want to become serious advocates or donors, or to simply review the proposal should contact me.

    If we can digitise and simulate the challenge then we would radically accelerate the detection of candidate solutions that are likely to fail in terms of safety or efficacy. This means the above phases of a clinical trial could be dedicated to very promising candidates, speeding up the net clinical process.

    A giant leap forward would require an accurate digital model of the human cell (including all its types). Easier said than done, but some progress is being made.

    Once we have a digital representation then simulated tests can take place extremely quickly to weed out candidates with a low probability of success.

    We can go even further, using AI [and later quantum computing] we can rapidly derive new promising candidates. There has been some success in applying AI to this field, and much greater breakthroughs are expected.

    Beyond the digitally simulated world, we can use AI and lab robots to grow cultures and mini-organs [perhaps 3D printed] to test the safety and efficacy of candidates. A closely controlled and monitored lab environment might also shed important details on the experiments and allow the AI to learn why candidates fail, and to derive improved solutions.

    It’s important that this data is shared widely [open data] to accelerate global solutions to many health challenges.

    [ PS: For those interested in surgically dealing with brain cancers here are some advanced and innovative microscopy and treatment suggestions: Brain Health Blog. ]