pharmacokinetic and pharmacodynamic drug interactions ppt to pdf

Pharmacokinetic And Pharmacodynamic Drug Interactions Ppt To Pdf

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If your institution subscribes to this resource, and you don't have a MyAccess Profile, please contact your library's reference desk for information on how to gain access to this resource from off-campus. Please consult the latest official manual style if you have any questions regarding the format accuracy. Drug interactions occur when one drug modifies the actions of another drug in the body.

Introduction to Drug Interactions.ppt

Drug-drug interactions DDIs constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints IPFs with successful application to DDI detection.

IPFs were generated based on the DrugBank database, which provided 9, well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia.

First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank.

Precision for the test sets ranged from 0. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Drug-drug interactions DDIs are a major cause of morbidity worldwide and a leading source of treatment inefficacy.

For this reason, DDIs cause great concern in patient safety and pharmacovigilance. Adverse drug events ADEs may occur when drug combinations target shared metabolical and pharmacological pathways altering the efficacy and safety profile of the drugs. Potential DDIs are evaluated for experimental drugs pre-clinically during development and then monitored by drug safety surveillance programs after they enter the marketplace.

The development of predictive tools to help study possible DDIs is of great interest to pharmaceutical companies and regulatory authorities, such as the United States Food and Drug Administration FDA [1].

These organizations are interested in better methods to detect and assess drug interactions [2]. Depending on the seriousness of the DDI, different measures are carried out ranging from the introduction of warnings in drug labels to the withdrawal of drugs from the market. As an example, in August the FDA [1] issued a warning about the possibility of developing rhabdomyolysis, a condition related to severe muscle injury, through combination treatment with simvastatin and amiodarone.

In contrast, mibefradil, a calcium channel blocker approved by the FDA [1] in June , was withdrawn from the market shortly after due to potential harmful interactions with drugs that prolong the QT interval [3]. Molecular fingerprints, digital representation of chemical features, are useful representations for comparing the structural similarity between compounds [10] — [13].

However, fingerprints could be designed to codify not only molecular structure information but also different biological properties. Following the concept of predictive models based on adverse drug event profiles [14] — [15] and comparing drug pairs through molecular fingerprints [12] , we developed a model to predict DDIs based on the comparison of, what we call, an interaction profile fingerprint IPF.

Two different interaction fingerprints can be compared using the Tanimoto coefficient TC , a general method for comparing the similarity of two sets [16]. Our motivating hypothesis is as follows: if drug i and drug j are similar according to their interaction fingerprints, then drug i will interact with the same drugs as drug j with a probability related to the similarity of their fingerprints and vice versa.

Figure 1 shows how the interactions of two drugs, oxybutynin and dicyclomine, are transformed into vectors, which are fingerprints, and then compared using the TC. The drugs associated with the non-intersecting interactions are predicted to participate in interactions with a probability proportional to the TC score see Figure 1. For example, we predict carbamazepine interactions with dicyclomine with a probability proportional to 0.

The similarity of both fingerprints is measured through the TC coefficient. The drugs corresponding to the non-intersecting interactions for the pair are assigned the TC score and form part of the prediction of the model. The effect associated by the interaction is the same as the original interaction source that generated the prediction. The model we developed combines the interaction profile similarity information using the DDIs specified in DrugBank to obtain new DDIs, but data from other sources could also be used.

The model results were validated using Drugs. We provided in the Table S1 of the Supporting Information a database with 17, DDI candidates predicted by the model along with the possible biological effects.

We collected the database from DrugBank [19] in a previous publication [4]. Only small approved drugs, not including proteins and peptides, were introduced in the previous model resulting in DDI information for drugs and a set of 9, unique DDIs.

Although we used the same dataset in the current article, improvements through future updates in the DrugBank database or the use of other important sources of DDIs, such as Drugs. This step would require an overall recalculation of the interaction profiles. The model included information about the pharmacological effect of the interaction associated with pairs of drugs as part of the process e. We represented all the drugs included in the study by IPFs.

The concept of IPFs is similar to molecular structure fingerprints [16] , [20]. The basic idea in IPFs is to represent the drug interactions for a particular drug as a vector codifying the presence of interactions in specific positions. As an example, in Figure 1 the interactions between oxybutynin and all other drugs are codified as different vector positions 33, 46, , , , , , Only the positions whose value is 1 are stored in vector-position notations. This is a very efficient way to represent a sparse binary matrix.

The same process is carried out for the drug dicyclomine that shares 7 out of 9 unique interactions with oxybutynin 46, , , , , , , The transformation of the molecules into IPFs facilitates comparison. We used the Tanimoto coefficient [16] , also known as the Jaccard index, to compute similarities between all the IPFs. We created a matrix so that the rows and columns represent drugs and each cell represents the interaction profile similarity based on the TC between the corresponding pair of drugs.

We computed this matrix using the MOE software [21]. To calculate the matrix M 3 with new predicted interactions, we multiplied the matrix M 1 Established DDI database matrix by the matrix M 2 Interaction profile similarity matrix see Figure 2.

It is worth noting that the values in the diagonal of the matrices M 2 and M 3 are 0 since the interaction of a drug with itself is not taken into account. Although the model could generate multiple scores for the same interaction based on similarities from different pairs, we only considered the predicted interaction with the highest TC value.

For this reason, in each cell of the product of the matrices, only the highest value in the array-multiplication is retained see Figure 2. We transformed the resulting matrix into the symmetric matrix M 3 considering the highest value TC for each pair of drugs. As an example, Figure 1 shows how we used a known interaction between haloperidol and dicyclomine to predict an interaction between haloperidol and oxybutynin.

The values in the diagonal of the matrices are set 0 since drug interactions with themselves are not taken into account. In the final matrix M 3 only the maximum value in the multiplication-array in each cell is preserved and a symmetry-based transformation is carried out retaining the highest TC value. The system generated a new predicted interaction between B and C with a TC score of 0.

We divided the database randomly in two sets: training and test sets. We used the Interaction Checker from Drugs. We calculated precision and enrichment factor compared to random selection see formulas in Table 1 for the four sets as measurements of the performance.

The results obtained by the model were compared to random expectations. We created a random system taking into account the list of 50 most frequently sold drugs in The top 50 list included 50 generic drug names but we only included 41 generic names in the system.

We cannot generate interaction predictions for these drugs so we removed them from consideration. These drugs were mometasone, ezetimibe, ferrous fumarate, naloxone, sitagliptin, latanoprost, insulin glargine, insulin aspart, and omegaacid ethyl esters. The number of possible interactions for 41 drugs in a matrix of drugs is 37, factorial 41 times. We estimated the number of positive cases as 7, interactions found in Drugs.

We combined similarity information by means of interaction profile fingerprint-based modeling with the initial database containing drugs and 9, DDIs, as described in the Methods section.

The final model generated a matrix of , DDI scores. We evaluated the performance of the model through hold-out validation and external test series. We performed two different evaluations by dividing the initial database into training and testing subsets. The stability of the model is barely affected even when we removed twice as many interactions. However, high performance in these sets is expected since the similarity matrix was generated using drug interaction profile information where the drugs and interactions in DrugBank have been specified and form a closed system.

For this reason, further evaluation of the model using independent test sets with no interactions previously collected in the initial DDI database is a necessary step to prove the prediction power.

We found 50 out of interactions in Drugs. The precision of this external set is 0. The performance of the model showed a 2. The precision in the second test set is 0. The model presents an enrichment factor of 2. In addition, we plotted the ROC curve taking into account as true positives all the interactions in the set confirmed in drugs.

The area under the curve is 0. Figure 5 shows the enrichment factor and precision achieved by the model for each drug. Out of the 50 drugs, we included 41 in the evaluation. Nine drugs were not taken into account because they were not included in our initial DrugBank DDI database and the model could not predict any interaction. Interactions for the top 50 drugs 41 generic names confirmed in drugs.

The test set of drugs are sorted according to the enrichment factor. Our method outperforms other commonly used approaches. In addition we tested if our model could predict pharmacodynamic interactions as well as pharmacokinetic.

A pharmacology expert manually reviewed and compared the pharmacological effect described in the predicted interactions for the test sets to the effect found in Drugs.

The interactions predicted by the model belong to two categories: some are generated comparing interaction profiles of pairs of drugs in the same pharmacological class, whereas the origin of other interactions resides in the comparison of the profile fingerprints of pairs of drugs that are not in the same class. For these test sets, the model generated the majority of the reviewed interactions through the comparison of pairs of drugs catalogued in the same or similar pharmacological class 48 out of 50 and 38 out of 43 for test A and B respectively.

As the TC values decrease so does our confidence in the predicted effect as these predictions result from comparing pairs of drugs with different pharmacological profiles. For the last test set D, we carried out a more challenging evaluation and only the effect of the interactions generated through the comparison of pairs of drugs belonging to different pharmacological classes was evaluated.

Out of the correct DDIs predicted by the model for test set D, were from comparing drugs belonging to different pharmacological classes. Table 2 describes some example predictions from the test dataset D detailed description is provided in Table S6 for which the model correctly detected interactions comparing drugs of different pharmacological classes as well as the effect produced by these interactions.

food-drug interactions 1.ppt

For example, a drug that causes chronic nausea or mouth pain may result in poor intake and weight loss. Movement of the drug from the site of administration to the bloodstream; depends on The route of administration The chemistry of the drug and its ability to cross membranes The rate of gastric emptying for oral drugs and GI movement The quality of the product formulation. Food, food components and nutritional supplements can interfere with absorption, especially if the drug is taken orally. Distribution When the drug leaves the systemic circulation and moves to various parts of the body Drugs in the bloodstream are often bound to plasma proteins; only unbound drugs can leave the blood and affect target organs Low serum albumin can increase availability of drugs and potentiate their effects. Primarily in the liver; cytochrome P enzyme system facilitates drug metabolism; metabolism generally changes fat soluble compounds to water soluble compounds that can be excreted Foods or dietary supplements that increase or inhibit these enzyme systems can change the rate or extent of drug metabolism.

Drug-drug interactions DDIs constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints IPFs with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9, well-established DDIs as a primary source of interaction data.


KEY TERMS: Pharmacokinetic interactions, pharmacodynamic interactions, absorption, distribution, protein binding, metabolism, drug metabolizing enzymes​.


Pharmacokinetic drug-drug interaction and their implication in clinical management

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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.

Drug interaction

Drug Drug Interaction phenomena that occurs when the effects pharmacodynamics or pharmacokinetics of a drug are altered by prior administration or coadministration of a second drug Hartshorn, EA, Tatro, DS: Drug Interactions, , Facts and Comparisons, St. Louis, MO. Most drugs must be lipid soluble to cross cell membranes and reach their site of action The net effect of drug metabolism is to increase water solubility and facilitate renal excretion Phase I metabolism primarily involves oxidative metabolism via the Cytochrome P CYP family of enzymes Phase II metabolism conjugates the previously oxidized molecule with a water soluble weak acid glucouronic acid, tauric acid, etc enhancing overall water solubility.

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Джабба тяжко вздохнул. Он знал, что Фонтейн прав: у них нет иного выбора. Время на исходе. Джабба сел за монитор. - Хорошо.

 - Она подошла вплотную к окну.

 - Позволь мне объяснить.  - Голос его, однако, мягче не.  - Во-первых, у нас есть фильтр, именуемый Сквозь строй, - он не пропустит ни один вирус. Во-вторых, если вырубилось электричество, то это проблема электрооборудования, а не компьютерных программ: вирусы не отключают питание, они охотятся за программами и информацией. Если там и произошло что-то неприятное, то дело не в вирусах.

Затекшая шея причиняла ему сильную боль. Такая работа была непростой, особенно для человека его комплекции.

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    In particular, poly-therapy increases the complexity of therapeutic management and thereby the risk of clinically important DDIs, which can both induce the development of adverse drug reactions or reduce the clinical efficacy.

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