Precision medicine aims to tailor disease prevention and treatment to fit people’s genes, environments, and lifestyles, targeting the right treatments for the right patients at the right time. It needs precision drugs with defined and validated molecular structures that interact with a precisely defined disease target to achieve this.
Precision medicine relies on tailoring disease prevention and treatment according to differences in people’s genes, environments, and lifestyles, aiming to target the right treatments to the right patients at the right time. However, it has not yet been clearly stated how this concept and goals could be achieved. The availability of “precision drugs” that are essential to make precision medicine possible is frequently not considered.
Precision drugs need to be defined and validated as having the molecular structure that would interact with a precisely defined disease target. Further, it must exhibit the right pharmacokinetics to facilitate the drugs’ efficacy. Ideally, onand off-target interactions should not cause disabling adverse events.
Precision drugs are yet to be discovered and developed. Drug discovery often starts with a “shot in the dark.” A huge number of compounds are often “filtered” in silico before being examined using high throughput screening to identify potentially relevant active compounds that are subsequently optimised and validated by establishing relationships between chemical structure and biological activity.
Once the disease indication has been selected, the process proceeds to identify the physiological mechanisms that need to be targeted and, ideally, a specific molecular ‘drug target.’ Drug discovery has largely relied on random in vitro screening of chemical compounds, in vivo animal studies, intuition, and serendipity.
Promising compounds are studied further for their toxicity and pharmacokinetics before clinical studies are conducted to generate the safety and efficacy needed before a drug can be approved for use in humans. However, the failure rate for compounds entering clinical studies to reach drug approval is greater than 90 per cent. So, perhaps, referring to the process as a “hit-and-miss” is not inaccurate. The above conventional activities do not provide the data needed for developing “precision drugs.”
The FDA approved several drugs described as “targeted”: Uptravi, Cosentyx, Cotellic, Odomzo, Xifaxan, Darzalex, Praxbind, Technivie, Opdivo, Alecensa, Empliciti, Keytruda, Ninlaro, Tagrisso, and Orkambi. None of these drugs act on a validated molecular target directly associated with the disease. Many “targeted” drugs have been approved to treat diseases. However, none of these drugs act on validated molecular structures responsible for the initiation and progression of the disease. Invariably, such drugs act on an element in a pathway known to be involved with the disease. However, some progress has been made in this direction.
One form of “targeted therapy” uses drugs that attack specific cancer cells. Such therapies usually cause less harm to normal cells than chemotherapy or radiation therapy. For example, chronic lymphocytic leukemia (CLL) is now treated successfully using monoclonal antibodies that attach to a specific target on cancer cells. The antibodies are able to then kill the cancer cells, block their growth, or keep them from spreading. Rituximab, ofatumumab, and obinutuzumab alone and in combination with chemotherapy are used to treat symptomatic or progressive, recurrent, or refractory CLL, targeting CD20, a protein found on the surface of B lymphocytes.
Immunotherapy has been hailed as one of the most promising new cancer treatments. It is expected to turn the power of the immune system — more powerful than any cancer drug —against cancer cells.
Immunotherapy is a treatment that uses a person’s immune system to fight cancer. It can boost or change how the immune system works to find and attack cancer cells. Substances made by the body or in a laboratory are used to boost, direct, or restore the body’s natural defenses against cancer. However, applying immunotherapy is not compatible with the precision medicine principle of “right dose for the right patient at the right time.”
The conventional drug discovery process has advanced our ability to treat many diseases. However, to advance to precision medicine, precision drugs are needed.
Immunotherapy is anything but precise. One of the reasons is the information a physician may have about the patient to treat.
While the premise of precision medicine requires detailed information about the biological mechanisms of the disease. At present, good biomarkers to predict a patient’s response to immunotherapy are not available. Further, good biomarkers to predict toxicities have not been identified.
Precision medicine requires the treatment to be applied at the right time. Adjuvant immunotherapy trials can last for 1–3 years, frequently not generating overall survival data. Such therapy puts an enormous therapeutic burden on too many patients for an unproven benefit.
The pharmacokinetics of precision drugs needs to be fully known and documented. Concerns have been expressed that patients receive unnecessarily high doses of immunotherapy. Several studies have shown that a dose-response relationship is often not established with immunotherapy. For instance, peripheral receptors may become saturated at a dose of 0.3 mg/kg of nivolumab. However, giving a lower dose is not in the financial interest of the healthcare business ecosystem, so a “personalised” weightbased dosing is replaced by a flat dose, irrespective of weight.
Similarly, the frequency of dosing is often not supported by data and may be too frequent. For example, during the pandemic, regimens were developed to allow patients to get the care they needed but at fewer intervals, limiting their contact with the healthcare system, for instance, changing from every three weeks to every six weeks. However, despite knowing there is no dose-response relationship with immunotherapy, the manufacturers decided to double the doses. Using precision drugs could make promising immunotherapy treatment more precise and effective.
Without recognising the need for precision drugs and exactly defining what is needed, precision medicine is either a fantasy or bad propaganda.
In developing disease-targeted drug therapies, it is critical to make the task very clear and define it in terms of unique molecular structures present in specific disease-associated cells. However, a question does remain: “Can this be done using the approaches employed so far?”
A new paradigm for developing truly disease-targeted, precision drugs needs to be developed and adopted to develop
• Approaches to identify any unique molecular structures present in a narrowly defined population of cells
• An effective algorithm for identifying such unique structures relevant to diseases (e.g., cancer).
Network pharmacology aims to understand the network interactions between a living organism and drugs that affect normal or abnormal biochemical function. It gathers data from pharmacology, network biology, systems biology, bioinformatics, and related sciences and uses the power of computers to identify possible interactions. This novel approach may be used to predict and identify multiple drug targets and interactions in disease. Network pharmacology-based drug design integrates systems biology, metabolomics, network analysis, and connectivity. This new paradigm enables the drugs to target several different proteins or networks implicated in a disease. The information generated by network pharmacology illustrates the complexity of diseases and could be employed in identifying molecular targets for precision-drug development.
This process will inevitably demand a major input from Artificial intelligence (AI). Such AI will need to match human “brain power” in many respects. The input will need to provide guidance on
• How to get and curate relevant data (published and perhaps even unpublished; regardless, all should be validated)
• How to validate that the algorithm is performing as needed, and
• How to improve the approach to answer the initial questions.
All this will require solid guidance from human-level intelligence for the AI to assimilate all the existing data on the topic, all the current assumptions and theories about how disease originates and progresses, and how it could be cured. The process will need to go beyond extracting information and draw novel conclusions and recommendations on what steps need to be taken to reach the ultimate goal–understanding the disease and defining unique molecular structures. The AI will need to acquire full capabilities in all aspects of human intelligence, such as being able to perceive the real world beyond the information given to it by its programmers. Further, it will need to have the capacity to determine the significance of various parts of the overall task and decide on which to focus. It will need to process information in human-thinking terms such as perception, abstraction, and memories, apply critical analysis of the information, and then remember and recall outcomes as and when needed to synthesize a new, more complex whole.
Ultimately, the AI will need to have the capacity to generate new knowledge and know-how. Will AI be able to speculate and imagine? It will need to. Will it be able to reason? It will have to. Will it be logical where humans often cannot be? Will it handle the “what if ” questions, predict, and make decisions? All that and more. It would better be. However, intelligent human input will likely be needed all along the way to solve this challenging task.
References:
Petrak K. The difference between targeted drug therapies and targeted-drug therapies. Medical and Clinical Research Reports, Vol 1. No 1, Nov 8, 2018. Published by Eleyon.
Gaywall B. The great irony of modern oncology: Immunotherapy imprecision. https://www.medscape.com/viewarticle/979707?src=WNL_infocu1_221119_MSCPEDIT&uac=296232CK&impID=4887214&faf=1 .
Petrak K. A New Paradigm for Developing Effective Anti-Cancer Therapeutics. Canc Therapy & Oncol Int J. 2017; 4(5): 555649. DOI: 10.19080/CTOIJ.2017.04.555649 .
Petrak K. A Challenge to Artificial Intelligence: Find Molecular Structures Uniquely and Functionally Connected to the Initiation and Progression of Diseases. ACTA SCIENTIFIC CANCER BIOLOGY (ISSN: 2582-4473) Volume 4 Issue Feb 2, 2020.
Karel Petrak obtained a D. Phil. degree at the Imperial Cancer Research Fund in London, England. He joined Novartis to develop advanced drug-delivery systems. Subsequently, he led research groups in developing systems to deliver genes and proteins. He now advises developing precision drugs using new paradigms for disease-specific drugs and their delivery.
