Cases On Drug Interaction And Adverse Reactions Pdf
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Adverse events are a common and for the most part unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs but also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets, and efficacious combination therapies.
- Drug Interactions and Adverse Reactions
- Drug Interactions and Adverse Reactions
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- Drug interaction
Drug Metabolism pp Cite as. A considerably detailed treatment of drug-drug interactions and adverse reactions has been presented above, together with older and more recent examples of each.
This paper describes the personal views of the author about diagnosis and management of an adverse drug effect. It proposes that diagnosis is complicated and is also supported by carefully observed management of changes in drug therapy. Drug-related adverse effects may be due to the drug itself, though many are due to systematic errors occurring in the process from diagnosis of the primary treated condition, through prescribing and dispensing, to the way the drug is used by the patient. Because of the multiplicity of definitions in the world literature, for clarity the following definitions are used in this article. Adverse effects and adverse drug reactions constitute major morbidity and sometimes mortality, but how to make a diagnosis and manage adverse drug effects in an individual to avoid or reduce serious harm does not receive much attention.
Drug Interactions and Adverse Reactions
Drug—drug interactions DDIs constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients.
We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods.
Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
Drug—drug interactions DDIs are a serious problem in patient safety [ 1 , 2 ]. Coadministration of two or more drugs at the same time can affect the biological action of the implicated drugs. The interaction can seriously affect efficacy and safety drug profiles. The main types of DDIs include pharmacokinetic and pharmacodynamic interactions [ 3 ].
The pharmacokinetic interactions can affect important drug processes that determine bioavailability, such as absorption, distribution, metabolism and excretion [ 3 ]. Examples of these interactions are the administration of a medication that increases the motility of the intestine decreasing the absorption of the other drug, competition for the same plasma protein transporter, inhibition of the action of a metabolizing enzyme or even interaction at excretion level affecting the elimination of one of the drugs [ 4 ].
On the other hand, pharmacodynamic interactions can occur at the pharmacological receptor level with both drugs interacting with the same protein, at the signaling level affecting different signaling pathways or at the effector levels causing different pharmacological responses [ 5 ]. The action can be synergistic or antagonistic or a novel effect can be derived. DDIs result in many adverse drug effects ADEs that can cause severe injuries to the patients, or even be responsible for deaths [ 6 ].
Hospitalizations and emergency department visits because of coadministration of different drugs are estimated around 0. Moreover, the numbers and statistics about DDI impact in health care may underestimate the real public health burden because the statistics are based on known DDIs, and unknown DDIs may be significant. Once a DDI is well described, and depending on the danger associated with the interaction, further actions will be considered that go from a warning notice in the label of the drugs to the withdrawal of some drugs from the pharmaceutical market.
For instance, cerivastatin, a drug that lowers cholesterol levels by inhibiting the enzyme HMG-CoA reductase, was withdrawn from the pharmaceutical market worldwide in because of 52 cases of fatal rhabdomyolysis [ 11 , 12 ].
The coadministration of cerivastatin with other medications, such as gemfibrozil, was responsible for the death of several patients.
Gemfibrozil reduces the metabolization of the statin, increasing its plasma concentration and causing a higher risk of associated adverse effects like myopathy and rhabdomyolysis [ 13 , 14 ]. Another example of a drug withdrawn from the market is mibefradil, a calcium channel blocker, 1 year after approval by the US Food and Drug Administration FDA [ 15 ] because of risk of deadly interactions with other drugs [ 16 ].
These reports showed the importance of taking into account by prescribers and healthcare professionals, some well-known DDIs that have a great impact in the generation of ADEs in the patients [ 19 ]. Being aware of these dangerous combinations by medical personnel and not prescribing combinations of drugs that cause serious adverse effects is important to improve health systems [ 20 ].
A more conscious knowledge of the importance and incidence of DDIs, as well as the most dangerous drug combinations is a crucial feature to avoid their occurrence, reduce their clinical impact and collaborate in the safety of the patients. Systems in clinical decision-making that warn physicians about potential drug combinations are valuable tools to improve health care.
However, problems related to alert fatigue, when many potential warnings are given, can undermine the usefulness of the technology [ 21 , 22 ]. Decision systems should prioritize high-risk DDIs to make the number of alerts manageable by the professionals.
Development of a consensus DDI list by experts and endorsement by professional societies, agencies and regulators help to create improved health care decision-making systems [ 23 ]. Moreover, avoiding possible DDIs evaluating high-risk factors, such as age, multiple diseases or genetic polymorphisms [ 24 ], should be a common practice oriented to enhance patient care. In case multiple and complex therapies are necessary, a better system to specify to the patients which drugs cannot be taken together would be helpful.
In this sense, enhancements in the development of tools to facilitate the administration of multiple therapies to patients are welcome to improve clinical decision-making and patient safety. DDIs are difficult to detect in the different stages of drug development and in postmarketing surveillance [ 1 ]. Some DDIs could also be dependent and recognizable at high medication doses [ 25 ].
Clinical trials use a limited number of patients and include different criteria for inclusion or exclusion of the participants, with the consequent limitations to deeply examine the effects of polypharmacy.
Moreover, human variability can affect the result of DDIs [ 1 ], and adverse effects could occur in a certain subgroup of the population not properly represented in a clinical trial. Long-term follow-up provided by data mining of public scientific literature and clinical pharmacovigilance sources could help to overcome some obstacles derived from short-term studies like clinical trials [ 26 ]. Improvement in the detection of DDIs is of major interest to regulatory organizations, such as the FDA [ 15 ], pharmaceutical companies and a broad group of researchers working in translational medicine and drug safety.
Besides a more realistic detection, methods that provide understanding of the mechanisms or principles for the DDIs are also needed. A more detailed description of the different computational and experimental techniques used to discover DDIs is provided in some articles and reviews already published [ 1 , 27—31 ]. Coadministration of multiple drugs could also be responsible for synergistic effects, such as an increase in the efficacy of the therapy.
In fact, this is a common practice in medicine that in some cases offers great results in efficacy, avoiding toxicity or minimizing drug resistance [ 32 , 33 ]. However, in this review, we focused on the detection of DDIs caused by the coadministration of multiple drug regimens that are responsible for adverse effects in the patients. We reviewed data mining studies that use clinical databases, such as electronic health records EHRs , the scientific literature and social media tools. We provided a general picture of the usefulness of the different sources and their advantages and limitations in the extraction and detection of DDIs.
We showed multiple examples where pharmacovigilance data, scientific literature and social media were used not only in the detection of new DDIs but also to prove the important impact caused in health care and to extract DDI knowledge crucial for the development of reference standards to evaluate detection tools.
The different topics discussed in the current article are shown in Figure 1. As a summary, this review contains different sections focusing on data mining of pharmacovigilance sources, scientific literature and social media, along with challenges and limitations of the different DDI sources. Flowchart of the different steps described in the review. Applicability of data mining using different sources: applicability showing the importance of DDIs as the cause of ADES, in the detection of novel DDIs and in the development of knowledge databases.
DDIs are a real problem in clinical practice worldwide. There are different pharmacovigilance studies that reflect the importance of DDIs as the means by which patients develop ADEs. As an example, Montastruc et al. Moreover, a triple combination therapy receiving two antihypertensive treatments with NSAIDs was associated in a nested case-control study with increased risk of acute kidney injury [ 36 ].
In another study, one-third of the interactions detected with cholinesterase inhibitors were estimated to be the cause of adverse effects [ 37 ]. Many potential ADEs caused by the concomitant use of drugs were also detected in the spontaneous reporting system SRS from Italy [ 38 ]. The study showed the importance of DDIs involving anticoagulant drugs as a risk of serious adverse effects.
The adverse effect bleeding was as well evaluated by Schelleman et al. Patients on the anticoagulant warfarin that initiated a concomitant therapy with some antidepressants had a higher risk of hospitalization because of gastrointestinal bleeding. Odds ratios for warfarin users with a combined therapy with the drug citalopram, fluoxetine, paroxetine and amitriptyline were 1. The impact of DDIs as a cause of adverse effects was additionally reported by Mirosevic Skvrce et al.
The distribution of the different adverse effects caused by the DDIs is shown by Mirosevic Skvrce et al. Moreover, DDIs have an important impact not only in the generation of adverse effects but also in the efficacy of some of the implicated drugs, with the associated risk for the patients. As an example, there are studies that showed an attenuated effectiveness of antihypertensive drugs when administered with NSAIDs [ 43 ].
There are also some excellent reviews available in the literature that showed the impact of the adverse effects caused by DDIs in the health system and clinical care [ 2 , 44 ]. The FDA [ 15 ] receives reports of suspected adverse effects from health care professionals, consumers and manufacturers. Reports in FAERS contain patient, drug and adverse effect information and include many medications with the suspected drug and concomitant drugs drugs are labeled as primary suspect being the cause of the adverse effect, secondary suspect, etc.
Calculations of some algorithms are based on two-dimensional contingency tables see Figure 2 with a summary of some DMAs. Multivariate modeling, such as logistic regression, has also been used to analyze the effects of drugs [ 48 ]. There are in the literature some reviews [ 49—51 ] that explain with more detail the different data mining algorithms described in the published studies.
In fact, there are several examples describing potential DDIs with reporting sources and completing the study through a validation performed in EHRs [ 53 , 54 ]. In many cases, the analysis looking for DDIs is based on structured data, although unstructured information that resides in the clinical notes taken by the medical personnel in the EHRs can also be useful.
There are some examples that showed that coded data may be insufficient to describe some steps in pharmacovigilance procedures and that the combination with narrative data can yield better results, such as building patient cohorts [ 55 , 56 ]. The combination of structured and unstructured data can be exploited to detect DDIs and ADEs with higher confidence [ 57 ]. In this sense, increasing access to medical records may facilitate the detection and validation of adverse effects caused by the use of concomitant drugs.
Nevertheless, there are some studies mining large data sets with the aim of looking for many diverse potential DDIs. As an example of these studies, a DDI signal detection algorithm was published by Tatonetti et al. The authors divided FAERS into two data sets: reports with only one drug training and reports with two drugs prediction , and constructed eight clinically significant adverse event models.
In each model, they described a drug as a profile of the adverse effect frequencies extracted from FAERS and through a logistic regression classifier differentiated between drugs that cause the clinically significant adverse event under study and drugs that do not cause the adverse event.
Then, predictions in drug combinations were made for each model. Their DDI method pointed out that pravastatin and paroxetine could contribute to elevated blood glucose levels, and the possible interaction was assessed in experimental assays [ 65 ].
Their system outperformed an alternative direct evidence model. As an example, ceftriaxone and lansoprazole could prolong the QT interval.
Other pharmacovigilance reporting systems were also used in the detection of potential DDIs, such as the WHO database [ 52 ]. The study proposed a disproportionality measure based on an additive risk model. A different study of drug hepatic safety [ 69 ] after coadministration of multiple medications was also carried out in the WHO VigiBase TM.
Identification of DDIs was carried out through empirical Bayes geometric mean values [ 47 ]. Liver event terms were created using the Medical Dictionary for Regulatory Activity [ 70 ]. Co-reported therapies were associated with changes in the frequency of hepatic events. All these studies reflect the importance of reporting systems as a source of analysis and follow-up to discover novel DDIs.
For instance, Iyer et al. The study also estimated the rate of adverse effects for the patients on multiple drugs, useful for alert prioritization in DDI surveillance and clinical decision-making.
The authors tested their algorithm in single drugs nephrotoxic and non-nephrotoxic , and the system was applied to 45 pairs of non-nephrotoxic drugs finding some combinations interesting to further study. A different approach was described by Pathak et al. They represented patient data as labeled graphs in a Resource Description Framework. They performed a case study with cardiovascular drugs in FAERS and carried out a signal enrichment using data extracted from medical records.
Other examples that demonstrate the usefulness in DDI detection of patient electronic data along with temporal association methodologies are provided in the literature [ 77 ]. The development of improved methods to score the DDIs studied in the pharmacovigilance data is important to eliminate possible false-positive cases and prioritize the candidates for further investigation.
Drug Interactions and Adverse Reactions
A drug interaction is a change in the action or side effects of a drug caused by concomitant administration with a food, beverage, supplement, or another drug. A cause of a drug interaction involves one drug which alters the pharmacokinetics of another medical drug. Alternatively, drug interactions result from competition for a single receptor or signaling pathway. Both synergy and antagonism occur during different phases of the interaction between a drug, and an organism. For example, when synergy occurs at a cellular receptor level this is termed agonism , and the substances involved are termed agonists. On the other hand, in the case of antagonism, the substances involved are known as inverse agonists. The risk of a drug-drug interaction increases with the number of drugs used.
Drug—drug interactions DDIs constitute an important concern in drug development and postmarketing pharmacovigilance. They are considered the cause of many adverse drug effects exposing patients to higher risks and increasing public health system costs. Methods to follow-up and discover possible DDIs causing harm to the population are a primary aim of drug safety researchers. Here, we review different methodologies and recent advances using data mining to detect DDIs with impact on patients. We focus on data mining of different pharmacovigilance sources, such as the US Food and Drug Administration Adverse Event Reporting System and electronic health records from medical institutions, as well as on the diverse data mining studies that use narrative text available in the scientific biomedical literature and social media. We pay attention to the strengths but also further explain challenges related to these methods. Data mining has important applications in the analysis of DDIs showing the impact of the interactions as a cause of adverse effects, extracting interactions to create knowledge data sets and gold standards and in the discovery of novel and dangerous DDIs.
Background: Potential drug–drug interactions (DDIs) are frequent in drug prescription but often responsible for adverse drug reactions (ADRs) and may lead to traindicated associations and major pDDIs (75 cases), followed by September 13th bpwnjfoundation.org
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In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions DDI has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training.
A drug-drug interaction may increase or decrease the effects of one or both drugs. Adverse effects or therapeutic failure may result. Rarely, clinicians can use predictable drug-drug interactions to produce a desired therapeutic effect. For example, coadministration of lopinavir and ritonavir to patients with HIV infection results in altered metabolism of lopinavir and increases serum lopinavir concentrations and effectiveness.
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. In addition to adverse events caused by use of a single drug, adverse events can be caused by drug interactions.
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Смит был прав. Между деревьев в левой части кадра что-то сверкнуло, и в то же мгновение Танкадо схватился за грудь и потерял равновесие. Камера, подрагивая, словно наехала на него, и кадр не сразу оказался в фокусе. А Смит тем временем безучастно продолжал свои комментарии: - Как вы видите, у Танкадо случился мгновенный сердечный приступ. Сьюзан стало дурно оттого, что она увидела.
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