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Shubhankar Kulkarni Jan 28, 2021
How can we weigh the efficacy and side-effects of a drug?
Image credit: Photo by Anna Shvets from Pexels
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We know that drugs come with side-effects. How can we determine the severity of these side-effects? We usually determine the efficacy of the drugs, that is, how much "benefit" does an XYZ dose of a drug has on our body. For example, metformin brings down the HbA1C levels by about 1 to 2%.
Similarly, how can we measure the side-effects of a drug? How can we compare the efficacy of a drug with its side-effects before consuming it?
The side-effects are varied and can be both physiological and psychological. Is there a way to normalize them?
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Creative contributions
How to know which side-effect is valid? Solution: "Reporter" in every drug
The problem
Side effects can be insignificant or fatal, short-term or life-long, but what I want to point out here is that they can occur when taking the drug as prescribed or by improper use.
Are all those valid or some of them should be called "side effects of the improper use"?
The improper use considers:
- incorrect dosage
- taking two drugs with known interactions simultaneously
- other off-label use
The question
If the patients are not under 24/7 surveillance while reporting side effects, should we believe their questionnaires blindly? Can we somehow find out if the patient was using the drug as prescribed?
Should they be subjected to the lie detector while answering the questionnaires?
Why is this important
This is important because, for example, some people will die after taking the COVID-19 vaccine. That will probably be the immunodeficient, or people with other health issues; rarely the healthy ones. Should death be written on the vaccine label as one of the side effects then or not? Should they have taken the vaccine or not? How to evaluate this? By questionnaires? That means that the pharmaceutical industry is based on the balance between beneficial and adverse effects based on subjective answers in the questionnaires. In times of increasing computing and AI power, this is not acceptable, (I think).
The solution
The idea to solve this could be the implementation of a reporter (dye, inert protein, chemical) in every pill, vaccine, or syrup given to a patient. It should be universal for all drugs and applications, standardized, and easy to be traced inside the organism. When a patient reports a side effect, the concentration and localization of the reporter that came in the same pill could easily show if the person used the drug as prescribed. If not, the side effects should be considered a result of improper use. That way the pharma industry could dissociate itself from these fatal cases caused by the incorrect application and at the same time point out the importance of proper drug usage by the consumers. It could help to lower the rate of antibiotic misusage that recently caused many problems with super-resistant bacteria.
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Computational molecular docking can be a useful technique
With growing ailments, we surly need new drugs. Unfortunately, drug trials is a lengthy procedure, and it took years for a drug to become prescription-able. Moreover, the side effect prediction methods mainly focused on drugs' chemical and biological properties do not seem to be a good approach. Therefore, computational modelling methodologies are definitely a good alternative to conventional methods of drug testing. A few references are listed below, in which high-throughput computational molecular docking techniques have been developed to check the efficacy and side effects of target drugs using machine learning approaches.
- Prediction of Side Effects Using Comprehensive Similarity Measures
- A Novel Triple Matrix Factorization Method for Detecting Drug-Side Effect Association Based on Kernel Target Alignment
- Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
The integration of existing knowledge of drugs, mathematical modelling, and machine learning could be a potent solution for rapid development of new drugs.
[1]https://www.hindawi.com/journals/bmri/2020/1357630/
[2]https://www.hindawi.com/journals/bmri/2020/4675395/
[3]https://www.hindawi.com/journals/cmmm/2020/1573543/
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Shubhankar Kulkarni3 years ago
Thank you for the contribution Nitish! All the papers you have cited explain ways that help predict the side-effects of a drug. What I am looking for is solutions to measure these side-effects. As a simplistic example, what I want to do is assign a number to each side-effect based on its impact on a person's life - if mild headache is assigned a value of 1, moderate 2, severe headache will be 3. Recurring episodes of headache will score more according to this measurement. Now, this was regarding a single side-effect - headache. If we were to expand this to other side-effects, how could we go about it? If headache was assigned a value of 1, what would nausea be?
However, I think this suggestion of yours can be modified slightly and posted on another session on the ways artificial intelligence can be used to improve aging research and medicine (https://brainstorming.com/sessions/in-what-ways-can-ai-be-used-to-improve-longevity-research-and-or-medicine/266). I will add it there.
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Nitish3 years ago
Shubhankar Kulkarni Sir, I understand, you want to construct the severity chart of drugs for their probable or known side effects. But one thing which needs to be considered here is the side effects of drugs on metabolism of a single individual. I think it is not only the drug that exclusively determines its efficacy or ill effect, but the metabolism of the host also has a vast role to play. Therefore, drugs' side effects can vary largely on the individual level. And It would be more speculative than predictive to assign a severity index to a drug or a combination of drugs.
I will surely try to start a separate session on this.
Thank you!
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Shubhankar Kulkarni3 years ago
Nitish Yes, the severity chart should be able to compare different intensities as well as different side-effects altogether.
I entirely agree with your point that side-effects may vary largely on the individual level. There may be inter-individual differences in the reactions to the same drug. Are the studies that you mentioned in your suggestion capable of detecting these inter-individual differences? Correct me if I am wrong but I thought these studies have built tools that try to identify the side-effects in humans in general. Since these tools take the docking, drug-effect, and drug-drug association data from sources that do not display data at the individual level, I think these tools would not be able to determine which individuals may show a particular side-effect and which may not.
Also, the side-effects vary between individuals in terms of intensity, too. That will be really difficult for the tools to predict with the information they use.
Also, although the side-effects affect different individuals differently, there are some effects of the drugs that affect most or all individuals. After all, the major beneficial effect of the drug is common to most. Similarly, there exist side-effects that are common to most.
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The problems "behind the scenes"
To understand the whole thing better and find a universal scale of the impact of drug side effects on human life, I think it's important to first elucidate all the problems of current side-effects measuring and presenting methods.
- Side effects presented as a minor problem compared to the benefit of a drug
The information about drugs reaches us in multiple forms, which allows companies to "dose the harmful information wisely". In a 15 second TV ad, you will hear about the benefits of the drug, but not the side effects. It should not be like this, since most of the drugs targeting similar or the same problem are weighted according to the efficiency and side effects. Also, the repeated sentence about contacting your doctor or pharmacist about side effects is written in extremely small font or said in a lightning-fast way, which results in no one paying attention, until you get a serious side-effect problem.
2. A mismatch in side effect interpretation
Terms that describe side effects (a.k.a. adverse events) appear in various drug information sources such as the FDA-approved drug labels, United States Pharmacopeia Drug Information and user-friendly pharmacy leaflets, TV ads, and the Internet. The inconsistency of the usage of the terms makes it hard for people to understand the side effects themselves, and that's why most people just ignore it. The researchers from Duke University described this problem in their 2006 paper . Terms describing the frequency and severity were presented differently in multiple information sources (Figure 1.) and tested by comparing two approaches (evaluating the Numerical and Visual Tasks group results). The results showed that in a sample of 222 Duke undergraduate students, there is no drastic difference between the interpretation of differently presented side effect data. Although the results were positive, the problem could become more prominent in the general population, where fewer people can distinguish and evaluate specific terms used inside effect description.
Figure 1. The inconsistency of the terms describing the side effects. Taken from Hubal and Day (2006) .
3. Subjectivity in the evaluation of side effects
Some side effects, like headaches, vivid dreaming, or tired legs, can be evaluated very subjectively due to a person-specific interpretation of pain or other symptoms. For now, these are being evaluated by the questionnaires filled by the patient. Although numeral scales (1-10) are more frequently being replaced by the visual ones (Figure 2.), they still do not cover the whole extent of the side effect (not just the intensity of pain, but also the frequency, length, specific location, pattern of occurrence, ...). We need or more detailed visual analog scale questionnaires, or the other, more intelligent universal golden standard for side effect evaluation.
Figure 2. Example of the visual analog scale used for side effect assessment (taken from https://www.physio-pedia.com/Visual_Analogue_Scale).
Feel free to add or comment on these. It's just an attempt of defining the core of the problem.
[1]https://www.aaai.org/Papers/Symposia/Spring/2006/SS-06-01/SS06-01-012.pdf
[2]https://www.aaai.org/Papers/Symposia/Spring/2006/SS-06-01/SS06-01-012.pdf
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Shubhankar Kulkarni3 years ago
Hi Juran K. Thank you for your contribution. I agree with this completely.
Every drug has an FDA label attached to it, which contains reliable information regarding the composition, the use, the contra-indications, and the side-effects of the drug. However, as you pointed out, the media that present us the information regarding a drug do not need to include all that information in detail. Even if they mention them, it is in a tiny font. Also, even the drug labels do not contain the necessary information regarding the "intensity of pain, the frequency, length, specific location, pattern of occurrence" of the side-effect. Now that I think of it, even most clinical trial reports do not mention the intensity, frequency, and pattern of the side-effects. So basically, such information is not even noted. If it is noted, it is not made public.
One of the problems behind this may be the ignorance that comes with the word "side". The general norm is that side-effects are not mainstream effects and "should be" tolerable in light of the benefit that the drug brings. The second problem is we do not have a universal side-effect measuring system that attaches a cumulative score to a drug based on all its side-effects. Since we have not been able to measure different side-effects using the same scale, attaching such a score is currently not possible. This feels like a chicken and egg problem. Although this is not the only reason behind the problem, maybe if someone comes up with a reliable scoring system, such data could be noted.
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How can we be completely sure when we attribute a side effect to a drug?
This is an issue that I have thought about often. When describing drug side effects, we say that they are unintended, normally negative, effects that are observed after administration of a drug.
But how can we be sure that the drug causes that effect? For very common side effects that is easy, simple correlation can help us. If we give the patient drug A, 40% of them develop symptom 1. But correlation doesn't always mean causation, as shown by the coffee-pulmonary cancer paradox. That paradox is based in the fact that scientists found a statistical correlation between drinking coffee and pulmonary carcinomas. However, this was not a causative correlation. The simple fact was that heavy smokers also tended to drink more coffee. Thus, the real cause of cancer was the smoking, not the coffee.
Knowing this, the only way of being 100% sure of a side effect being caused by a drug is by studying the physiopathological process that has caused this. And that is not easy for all side effects, because some of them are very rare, very multifactorial or very difficult to explain.
Thus, the standard of practice is that, if a 1000 patients are enroled in a trial and 1 of them has a heart attack after being treated, I should investigate whether that can have been caused by the drug, or whether there is a clear cause not related to my drug. In case there is no conclusive evidence to either side, should I have to write that my drug could cause heart attacks?
I think this point has been put in the spotlight recently when talking about vaccine side effects, especially with the COVID vaccines. What are you guys opinions on that?
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Shubhankar Kulkarni3 years ago
I don't think one can ever be 100% sure while attributing an effect to a drug. There are a number of physiological pathways and their interconnections we don't know about. Improvement in one pathway can mean destruction in the other. That is why we attach a level/ threshold of significance to both the benefits as well the side-effects.
If 1 out of 1000 participants has a heart attack, more in vitro and animal studies may be needed to confirm that the drug has some influence on the heart. Also, there exist only so many signaling molecules in the body to perform a large number of functions. Multiple functions are attributed to a single molecule and multiple molecules may perform the same function, and the location of the interaction in the body adds another level of complexity. So, one drug will have multiple effects - some might be beneficial and some, not so much.
For the immediate side-effects, Juran K. 's idea of a "reporter" in every drug can be used. For effects that build over time, multiple factors may have contributed to the effect.
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Darko Savic3 years ago
Another factor to consider is that some undesired effects could build up over time. Correlating those to the drug would be even more difficult. Add to the mix the fact that the manufacturer of such a drug could be economically ruined via lawsuits and might attempt to use shady tactics in its defense.
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Measuring side effects objectively - PAIN
The problem
The golden standards for assessing pain are self-reported questionnaires or scales.
The problems of self-reporting are:
- the variability of assessment due to the patient/assessor´s psychological and physiological state (pain catastrophizing as an extreme example),
- the questionnaire text can make your pain assessment biased, and incomplete due to the assessor´s predispositions and points of interest and
- the impossibility to measure pain in patients with general anesthesia or with cognitive disorders
To solve these problems, we need to measure pain more objectively. Then we can use that for various purposes.
Solution - Neuroimaging + AI
Many studies tried to correlate data such as heart rate variability, blood pressure changes, skin conductance, pupil reflexes and other to subjective the feeling of pain . Although the results were good, neuroimaging turned out to be the best.
Previous neurostudies have shown that in patients reporting pain, certain brain cortices and subcortices activate. After the activation of noxious receptors by skin, muscle, or viscera stimuli, brain activity was reported in the secondary somatosensory cortex (SII), the anterior cingulate cortex (ACC), the primary somatosensory and motor (SI/MI) cortices, the cerebellum, the midbrain, and the insula (anterior, middle, and dorsal posterior regions), and the thalamus (Figure 1.). More regions have later been reported to be activated after the stimulus causing acute and chronic pain, confirming and revealing new pain assesment tools and data, such as an emerging dynamic pain connectome, which tries to describe the multidimensional pain experience .
Figure 1. The likelihood of evoking activation in the brain after the noxious stimuli shown on the ALE map. Taken from Duerden and Albanese (2013) .
In addition, paper have recently shown that spontaneous brain rhythms can be surpressed by pain . Also, researchers discovered that peak alpha frequency, that can be measured by EEG, correlated with subjective feeling of tonic pain . More data on factors measured by neuroimaging techniques such as functional MRI, resting state functional MRI, structural MRI, functional near-infrared spectroscopy , FDG-PET, SPECT, ASL and MEG showed promising results that could potentially revolutionize the pain assessment, if cleverly integrated and results combined by using the AI tools .
The expected final result
Universal pain assessment method (background noise removal, multiple housekeeping normalization, type-of-pain specific) and scaling system (combined index), based on neuroimaging techniques and AI.
The possible application - preclinical testing of drug side effects
The 2018 paper perfectly showed (except the correlation of spontaneous states of nociception and theta power) the application of neuroimaging techniques in identification of analgetic efficacy of drugs. Reasearchers used rat model to test the efficacy of certain pain-reducing drugs. By so called theta power assay for the quantitative assessment of spontaneous nociceptive state, they tracked the rat´s response to pain before and after the analgetic application.
Same could be done on many different models, given to an AI algorithm to learn the patterns and used in preclinical drug side effect assessment.
[1]https://associationofanaesthetists-publications.onlinelibrary.wiley.com/doi/full/10.1111/anae.13018
[2]Duerden EG, Albanese MC. Localization of pain-related brain activation: a meta-analysis of neuroimaging data. Hum Brain Mapp. 2013;34(1):109-149. doi:10.1002/hbm.21416
[3]https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979466/pdf/f1000research-9-22472.pdf
[4]Ploner M, Gross J, Timmermann L, Pollok B, Schnitzler A. Pain suppresses spontaneous brain rhythms. Cereb Cortex. 2006 Apr;16(4):537-40. doi: 10.1093/cercor/bhj001. Epub 2005 Jul 20. PMID: 16033927.
[5]Nir RR, Sinai A, Raz E, Sprecher E, Yarnitsky D. Pain assessment by continuous EEG: association between subjective perception of tonic pain and peak frequency of alpha oscillations during stimulation and at rest. Brain Res. 2010 Jul 16;1344:77-86. doi: 10.1016/j.brainres.2010.05.004. Epub 2010 May 10. PMID: 20460116.
[6]Fernandez Rojas R, Huang X, Ou KL. Toward a functional near-infrared spectroscopy-based monitoring of pain assessment for nonverbal patients. J Biomed Opt. 2017 Oct;22(10):1-12. doi: 10.1117/1.JBO.22.10.106013. PMID: 29076307.
[7]Zhang J. How far is arterial spin labeling MRI from a clinical reality? Insights from arterial spin labeling comparative studies in Alzheimer's disease and other neurological disorders. J Magn Reson Imaging. 2016 May;43(5):1020-45. doi: 10.1002/jmri.25022. Epub 2015 Aug 6. PMID: 26250802.
[8]van der Miesen MM, Lindquist MA, Wager TD. Neuroimaging-based biomarkers for pain: state of the field and current directions. Pain Rep. 2019;4(4):e751. Published 2019 Aug 7. doi:10.1097/PR9.0000000000000751
[9]Koyama, S., LeBlanc, B.W., Smith, K.A. et al. An Electroencephalography Bioassay for Preclinical Testing of Analgesic Efficacy. Sci Rep 8, 16402 (2018). https://doi.org/10.1038/s41598-018-34594-2
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Shubhankar Kulkarni3 years ago
This is helpful Juran K. This is one way to normalize all the side-effects - check the amount of pain they cause. I have some questions:
1. Is the amount of pain observed/ measured during brain imaging the same for people who can tolerate more pain and those who cannot? Does the level of tolerance of pain affect the measurement? It would be true normalization if the level of tolerance of pain did not affect the pain measurement.
2. A follow-up of the above question - If it is true that the level of tolerance of pain did not affect the pain measurement, is that pain measurement a just (or unjust) measurement? Here, both those who can tolerate pain and those who cannot show the same pain measurement due to a side-effect. I think it is a just measurement. The amount of pain due to a drug should not be dependent on any other parameter (like fitness) of the individual. What do you think?
This imaging method can account for all the side-effects that cause pain. What about those that do not? I don't think nausea causes any pain or does it? And do psychological side-effects cause pain that can be measured using this technique?
Anyhow, this technique will be highly useful and will take us multiple steps closer to our goal, if realized. This should be used in drug clinical trials and personalized medicine.
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jnikola3 years ago
Shubhankar Kulkarni I was not using the pain to normalize all the side effects, but the pain itself as a side effect (e.g. headache, arm/leg pain, belly ache, ...). Nausea probably has different approaches that can be used to measure the intensity, type, and other characteristics. If the described system was developed for every side effect, the network could be built and the general side effect index could be calculated.
I understand your concerns. It is true that a poor correlation was reported between the regional tissue damage and the subjective feeling of pain [1], meaning people feel pain differently.
Summary:
1 _ I think the level of tolerance (and other factors) affects the measurement.
2_ In my opinion, non-integrative neuroimaging itself is an unjust measurement.
________________________________________________________________________________________
1 _ I think the level of tolerance (and other factors) affects the measurement.
It was shown that repeated painful stimulation over several days resulted in substantially decreased pain ratings to identical painful stimuli [2] (measured by neuroimaging techniques). Not only that. Studies confirmed that doing a task while experiencing pain stimuli can reduce the pain experience [3]. Therefore, many things, not only tolerance, can change the experience of pain and parallelly, the neuroimaging measurements.
To explain this in detail, as mentioned in the paper [3], pain disrupts the behavior in various ways - by interfering with sensory, motor, and cognitive processes. Therefore, it's could easily be true that the same pain stimuli provoke the same patterns of nociceptor activity in all individuals (except the isolated cases of mutated receptors mentioned below), but results in different subjective feelings (neuroimaging measurements, as well as reports/questionnaires).
The supporting evidence for your proposed solution could be the paper stating that the identical brain activities evoked by pain were observed in healthy individuals and the ones insensitive to pain [4] (questionnaires + neuroimaging). But I think it is an isolated case of loss-of-function SCN9A mutations, which affect the nociceptor signal reception (neurochemical or other physiological defects). Also, the experiment was performed on 6 individuals, which is an insignificant sample size.
In addition to the neuroimaging techniques themselves, to normalize the pain intensity as a side effect, we should measure the nociceptor reception and activation directly, while taking into account possible mutations that affect them.
More experiments are necessary to distinguish the relation of the nociceptor activity and subjective experience of pain, but I personally think that neuroimaging techniques are affected by the level of tolerance (and other above-mentioned factors).
2_ In my opinion, non-integrative neuroimaging itself is an unjust measurement.
The second question refers to the fact that it would be interesting if we find a technique that can objectively measure the intensity of pain, without taking the subjective feeling and/or tolerance of the patient into consideration. If you read this again, it means that it doesn't make a lot of sense to believe that people can differently experience anything, but we should trust just the physiological/neurochemical proofs. Then it also wouldn´t be true that women are generally more resistant to epilation than men (which is really not true 22 ).
The pain is not a simple nociceptor expression, but a "psychobiology, attentional processes, and expectations of pain resulting from past and learned pain experiences" [1], and should therefore be evaluated individually based on the physiological and psychological parameters measured by the AI-driven neuroimaging techniques, normalized with a generally accepted cross-individually consistent features and correlated with subjective scale results.
[1] https://associationofanaesthetists-publications.onlinelibrary.wiley.com/doi/full/10.1111/anae.13018
[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5019436/
[3] https://pubmed.ncbi.nlm.nih.gov/20460116/
[4] https://pubmed.ncbi.nlm.nih.gov/27111250/
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Shubhankar Kulkarni3 years ago
Juran K. Thank you for the explanation! So, to conclude,
1. AI-assisted integrative neuroimaging coupled with nociceptor activation measurement can minimize the variance in the perceived pain of the side-effects. However, this will only work for side-effects that cause physiological/ neurochemical pain.
2. Certain mutations in nociception are a separate case.
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Side Effects are Extremely Complex and Patient-Dependent
Side effects are a complicated issue with most pharmacological treatments. Why are the prospects of some drugs large as the map of New York's subway system? Are those drugs really that dangerous? The answer is no, but they can be in the wrong circumstances.
The appearance of a side effect can be the result of a combination of factors, including but not limited to:
- Genetic background: Particular variations of cell receptors can interact differently with certain drugs: This can mean weaker/stronger as well as shorter/longer interactions, or new interactions that were not expected.
- Metabolization of drugs: Is based on processes that can themselves depend on the combination of several factors like activity of different liver enzymes, efficiency of import in different tissues or even unrelated pathological states of organs like the liver.
- Incorrect dosing: Doses for a lot of drugs are based in body weight and severity of symptoms which are not very scientifically precise measurements and do not take into consideration the patient's own biodistribution.
- The status of the body: especially the immune system and the microbiota can be severely affected by drugs if they are already subclinically altered. Similarly, damaged liver and kidney can present reduced processing and secretion of drugs leading to accumulation of the drug or toxic metabolites.
- Unexpected and unexplainable effects: these are very rare but normally severe reactions.
All of these factors are, by nature, intrinsic of every patient. For this reason, not all side effects are predictable or even measurable. Side effects can be classified in 5 types :
- Type A reactions are usually dose dependent and predictable, and are often recognised before a drug is marketed. Type A reactions are readily reversible by reducing the drug dose or withdrawing the drug. Many commonly documented ADRs are type A reactions. Type A reactions can result from the primary pharmacology of the drug, representing an exaggeration of the drug’s therapeutic actions.
- Type B reactions are unrelated to the known pharmacological actions of a drug and account for approximately 20% of all ADRs . Type B reactions are less common than type A reactions, but they are often serious and associated with high mortality. Type B reactions are often caused by immunological and pharmacogenetic mechanisms (genetically determined variability in response to drugs). Immunological reactions, such as anaphylaxis in response to penicillin, are classed as type B reactions
- Type C reactions, or continuing reactions, persist for a relatively long period of time, for example osteonecrosis of the jaw with the use of bisphosphonates
- Type D, or delayed reactions, appear sometime after the use of a medicine. The timing of type D reactions can make them difficult to detect. For example, lomustine, which is used to treat certain cancers, can cause leucopenia (a reduction in the number of white blood cells) up to 6 weeks after treatment starts
- Type E, or end of use, reactions are linked to the withdrawal of a medicine. For example, the withdrawal symptoms associated with the discontinuation of benzodiazepines for the treatment of anxiety can be prolonged and difficult.
As you can see, there can be a lot of different side effects and a lot of reasons for them. While market monitoring, pharmacogenetics and other approaches can help us better understand side effects, the fact they are so patient-dependent and, in some cases, unpredictable, makes it very difficult to assess the risks of taking a particular drug.
The questions that arise from this are: Does it make sense that pharmaceutical companies are obligated to report all found side effects of their drug in the prospect, even if their incidence is extremely low? Is that information really useful to the patient/doctor? How do we weight those unfreqüent but severe adverse reactions against the beneficial effects of the drug in most patients?
[1]Kaufman G. Adverse drug reactions: classification, susceptibility and reporting. Nurs Stand. 2016 Aug 10;30(50):53-63. doi: 10.7748/ns.2016.e10214. PMID: 27507394.
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Shubhankar Kulkarni3 years ago
Hi Manel Lladó Santaeularia ! By explaining the 5 types of side-effects, are you suggesting that only some of them should be studied and reported? There can be a priority list and the ones on the top should be mandatory for the pharma companies to study and report. The others such as the Type D (delayed reactions) can be added as and when they are reported/ occur.
I agree that measuring the side-effects is a difficult job and using the same scale for all, even harder. However, I think if we could measure them, it could help solve other related problems.
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Manel Lladó Santaeularia3 years ago
Shubhankar Kulkarni My point explaining the 5 types of side-effects was just to highlight the complexity of this issue. I don't believe one type is more important than others, as they are all very relevant and can have different importance for each drug.
I agree that it would be amazing to be able to measure all side effects, but my main point was that they are so varied and can depend on so many factors that it is a titanic effort
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Pay for both, positive and negative feedback
A 2020 study, which called-out the FDA, inspired Adam Pooling research on popular drugs for autistic children. The article published one year later reported research results on adverse effects of Aripiprazole (Abilify), a drug taht was introduced in 2009 and used to reduce aggressive behavior in autistic children. It was concluded that the adverse effects of the mentioned drug were not less severe than the competitor's - risperidone. Why did it take 11 years and thousands of reports to determine that the efficiency of a novel drug is not higher than the competitors?
One theory could be that it happens because of a great disbalance between money invested in marketing and sales, and pharmacovigilance, respectively. Once drugs are patented and on the market, companies just invest in ads and product's public faces. Adverse effects are put in the last drawer since their evaluation is time-consuming and gives companies time to make money. Often the third-parties, such as pharmacovigilance companies, do the reporting, but why not the manufacturers themselves take the enormous challenge? It's their responsibility.
FAERS (FDA Adverse Effects Reporting System) is the FDA's tool for post-marketing safety surveillance of drugs and therapeutic biologic products. It's a large database of adverse event reports, medication error reports, and product quality complaints. One thing that I found scary was that reporting data on drug safety is inputted manually by healthcare professionals, consumers, manufacturers and, third-parties. That leaves too much space for data manipulation, especially when you take into account that most of the reports are voluntary .
The solution could be pretty simple, I think. Agencies like FDA should oblige pharmaceutical companies to invest more money to produce and track drug safety reports once the product is on market (even after the patent expires (Figure 1). One idea could be to pay for every detailed mandatory or voluntary report. That way the professionals and other people who input data would be required to follow strict reporting rules, but would do it because of the "prize".
Also, on the FDA web page, it says the following: "Many factors can influence whether an event will be reported, such as the time a product has been marketed and publicity about an event.". It sounds like money is playing a big role here. The following graph supports the theory (Figure 1). Does it mean that if the adverse event was reported in the period before 2014, it is less likely to be publicly reported by the FDA?
Figure 1. Source: IQVIA National Sales Perspectives. Graphic by Krista Fuentes.
Investing a lot of money in marketing brings more money to the company, but it seems to be the opposite with drug safety. Can we change this?
The ultimate solution: Pay for positive feedback, too.
All the people who use the drug should report the adverse effects or the efficiency through the universal unbiased form. The FDA should then evaluate them and categorize the effects in "positive" or "negative". That way people would report, not only when the bad thing happens, but also when everything was good. That would give more reliable information to the FDA about the safety of a drug and pharmaceutical companies would invest their money, because, if everything is good, it could be a nice commercial on drug efficiency for them. All data should be public, too.
[1]https://www.fda.gov/drugs/surveillance/questions-and-answers-fdas-adverse-event-reporting-system-faers
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Shubhankar Kulkarni3 years ago
Juran Thank you! Yes, it seems like most of the rules favor money-making rather than pharmacovigilance. Maybe the FDA report was not made public before 2014, keeping in mind that after the patent expires, other companies can manufacture the drug, too. The sales, will then, obviously go down. Therefore, it would have been better for the drug if the negative report came after the expiry of the patent. This will keep the sales up during the period the drug has no competition.
I agree that FDA should enforce the rules regarding pharmacovigilance more strictly. I like the idea of paying for positive reviews, too. Most people, today, rely on reviews when buying anything. The only problem I see is that when it comes to drugs, as compared to other things, the patient may rely more on the doctor's prescription. And, the doctors are approached by the salesman employed by the companies. The salesman provides free drug samples and other perks to the doctors. This is a strong and influential relationship and does affect the sale of the drug. We might need to emplot alternative measures to tackle this part.
On a side note, who should pay for the reviews of the drugs - the companies of the FDA? I think it should be the FDA, which we consider to be unbiased.
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jnikola3 years ago
Shubhankar Kulkarni I completely agree with you.
I guess the FDA should pay for that. It makes more sense for the unbiased party to pay for it.
But, from the other standpoint, it makes no sense that a company invests a lot of money in marketing, but somebody else finances its feedback. It would make sense if the companies regulated by the FDA pay an increased fee, that covers the expenses. Shouldn't the parties that produced the drug be responsible for the management of the feedback? It's part of every business, right?
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