When drugs increase each others effects when taken together the interaction is called?

Drug-Drug Interactions

Pharmacokinetic and pharmacodynamic properties of drugs may be altered when ≥2 drugs are administered to a patient (Table 73.6). Interactions largely occur at the level of drugmetabolism but may occur at the level of drugabsorption (e.g., inhibition of intestinal CYP3A4 activity by grapefruit juice or St. John's wort and consequent reduction in presystemic clearance of CYP3A4 substrates), distribution (e.g., displacement of warfarin plasma protein binding by ibuprofen with consequent increased hemorrhagic risk), or elimination (e.g., inhibition of ATS of β-lactam antibiotics by probenecid). Also, drug-drug interactions may occur at the level of thereceptor (through competitive antagonism); many of which are intentional and produce therapeutic benefit in pediatric patients (e.g., antihistamine reversal of histamine effects, naloxone reversal of opiate adverse effects).

Drug interactions may also occur at a pharmaceutical level as a result of a physicochemical incompatibility of 2 medications when combined. Such interactions generally alter the chemical structure of one or both constituents and thereby renders them inactive and potentially dangerous (e.g., IV infusion of crystalline precipitate or unstable suspension). Ceftriaxone should be avoided in infants <28 days of age if they are receiving or expected to receive IV calcium-containing products, due to reports of neonatal deaths resulting from crystalline deposits in the lungs and kidneys. Alternatively, 2 drugs simultaneously administered perorally may form a complex that can inhibit drug absorption (eg., co-administration of doxycycline with a food or drug containing divalent cations).

Drug-drug interactions at the level of drug metabolism can be somewhat predictable based on a priori knowledge of a given drug's biotransformation profile. Although such information can be derived from the primary literature, it may not be immediately translated into a useful clinical context because of limitations associated with in vitro to in vivo extrapolation, including (1) use of animal models for characterizing metabolism; (2) extrapolating enzyme kinetics derived from pooled human liver microsomes or recombinant human drug-metabolizing enzymes to estimates of in vivo drug clearance; (3) extrapolating in vitro data obtained from fully competent (i.e., adult activity) hepatic microsomes to estimates of clearance in patients who may have developmental or disease-associated compromise in enzyme activity; (4) inaccurate accounting for pharmacogenetic variation in drug-metabolizing activity (i.e., constitutive activity) and the contribution of multiple different drug-metabolizing enzymes in overall drug biotransformation; and (5) the potential role of enzyme induction or inhibition in vivo that is not reflected by conditions used for in vitro metabolism studies.

Despite these limitations, information pertaining to a drug's impact on drug-metabolizing enzymes (e.g., substrate, inducer, inhibitor) can be useful in understanding if the drug has the potential to compete for, induce, or inhibit the metabolism of another drug (e.g., enzyme inhibition enhanced effect vs enzyme induction → diminished effect) of a drug-drug interaction. While multiple sources for this information exist (e.g., primary and secondary literature, drug product labeling), it may not be complete or updated. In examining multiple information sources pertaining to this topic, the authors have found the websitehttps://www.pharmgkb.org/ (accessed 21 February 2017) to be the most complete and useful for understanding drug metabolism pathways.

7 Conclusion

Potential DDIs are an important consideration from the perspective of the design and assessment of new drugs. The most common interactions include reversible inhibition, TDI, and induction of drug-metabolizing enzymes, as well as modifications in oral absorption due to co-administration with acid-reducing agents. Metabolism- and absorption-based DDIs can be differentiated by characterizing the pharmacokinetic behavior of victim compounds. The overall knowledge of the well-stirred model and physiologically-based pharmacokinetic (PBPK) models have increased and as a result, the understanding of metabolism-based DDIs has shown large improvements. Adaptation of mechanistic models for drug inhibition and induction presented in this chapter within the dynamic framework of PBPK models has emerged in recent years and advanced the science of quantitative prediction of metabolism-based DDIs. Despite the advancements in quantitative prediction, potential DDIs require confirmatory assessment in clinical trials when a risk is identified. With the prevalence of polypharmacy ever rising, DDI assessment will remain a crucial part of the characterization of new drug candidates.

Drug-Drug Interactions

Drug-drug interactions are categorized into PD and PK interactions. Many interactions deemed PD are really the exposure-response effects of a PK interaction leading to greater pharmacologic effect. These PD interactions are often exploited in antiinfective therapy, and it is through this mechanism that combination therapy can demonstrate synergistic effect. Quantitation of PD interactions is at best difficult. PK interactions are somewhat more “predictable” but still show large variability in the population.

PK drug-drug interactions can occur through a number of different mechanisms. These include:

Reduction of drug absorption by concurrent drugs: The most common absorption interactions occur by means of chelation of an antiinfective with a cation such as calcium, magnesium, or iron. A useful rule of thumb is that oral antiinfectives should not be administered within 1 hour before or 2 hours after the administration of oral divalent or trivalent cations unless a study has clearly demonstrated a lack of significant interaction with their coadministration.

Displacement from protein-binding sites: Although this interaction in theory could enhance antiinfective tissue distribution and efficacy, homeostatic mechanisms act to increase the elimination of unbound drug, resulting in no substantial change in overall drug exposure.

Inhibition, induction, or activation of DMEs or transporters: These drug interactions are by far the most important seen in clinical practice. Most information on drug-drug interactions is generated in a very limited number of individuals, often healthy volunteers, and may not be applicable to the general population. Some basic tenants of these drug interactions are important to remember:

Genetic and environmental factors, in addition to the microbiome composition, can affect the baseline activity of DMEs and transporters and thus will affect the potential for drug-drug interactions.

Not all patients experience a drug-drug interaction, even if this has been described in the literature. This is determined by both genetic and environmental factors.

Patients with low or null DMEs or transporter activity have a low chance of drug-drug interactions; however, if this low baseline activity is due to a reversible pathologic process, the potential for drug-drug interactions will increase as the pathologic process is appropriately treated.

Patients with normal or high DME or transporter activity have a high likelihood of drug-drug interactions.

Patients with a low genetic potential to make a DME or transporter (poor or intermediate metabolizers or transporters) have a small chance of an induction interaction because they do not have the genetic potential to make more of the enzyme or transporter when stimulated to do so.

Patients with normal or high genetic potential to make a DME or transporter are at greater risk of an induction interaction owing to the greater genetic potential to make the enzyme or transporter.

Patients can undergo enzyme heterocyclic activation of a DME, which increases the activity of the enzyme approximately 33%. In enzyme heterocyclic activation the peak effect occurs immediately, whereas it can take up to 2 weeks for the peak effect to be seen in induction. Heterocyclic activation is most often seen with CYP3A isozymes and CYP2B6.

Over the course of many infectious diseases, patients with infections (acute and chronic) have the potential to substantially change over time in terms of their risk of drug-drug interactions; thus this is not a static process.

Flucloxacillin

Drug–drug interactions Coumarin anticoagulants A 68-year-old woman taking long-term warfarin for atrial fibrillation had an acute left hemiparesis after taking oral flucloxacillin 500 mg qds and oral phenoxymethylpenicillin 999 mg qds for a soft tissue infection for 17 days [51A]. The International Normalized Ratio (INR) was 1.4, having been 3.5 before 19 days. Interactions of warfarin with penicillins are uncommon and are usually associated with increased anticoagulation. It seems more likely that the outcome in this case was associated with variable adherence rather than a drug–drug interaction.

Drug-Drug Interactions Between ART and Hormonal Contraceptives

Another potential concern raised with hormonal contraceptives and HIV is the potential for drug-drug interaction between the hormonal contraceptive and ARVs, resulting in a decrease in systemic ART exposure or exogenous hormone concentrations. A few pharmacokinetics studies have found that exogenous hormone exposure influenced ART exposure. One study found statistically lower efavirenz (EFV) concentrations when given with an oral contraceptive pill containing ethinyl estradiol/desogestrel.173 Another study of the contraceptive transdermal patch (ethinyl estradiol/norelgestromin) plus lopinavir/ritonavir–based ART identified significantly lower ritonavir exposures.174 Also, one study observed slightly higher nevirapine (NVP) and lower nelfinavir exposure when these ARVs were combined with DMPA.175 Despite these statistically significant changes, the changes in exposures were not lower than 80% of previously observed concentrations, and therefore the changes are unlikely to be clinically significant. In addition, most studies of drug interactions between more modern ARVs currently in use and hormonal contraceptives do not show a significant lowering in exposure to the former with the latter.176 Based on these data, the impact of hormonal contraceptives on ART exposure, if any, is small and unlikely to be clinically significant. Overall, the available data indicate that hormonal contraceptives do not impact the rate of disease progression for women living with HIV, either by primary impact on HIV disease or by secondary impact on ART pharmacokinetics.

However, clinicians need to be aware of the potential for pharmacologic interactions between most protease inhibitors and NNRTIs with both the ethinyl estradiol and the progestin components of hormonal contraceptives, as shown inTable 126.3.22,176 Both ethinyl estradiol and progestins are metabolized by the cytochrome P-450 (CYP) enzyme system. Generally, protease inhibitors are CYP inhibitors, and NNRTIs are CYP inducers, possibly leading to increasing hormone exposure and reduced hormone exposure, respectively. A reduced progestin exposure could impact contraceptive efficacy, as the contraceptive effect is primarily due to the progestin. An increase in progestin exposure is generally well tolerated. Reduced ethinyl estradiol exposure could lead to an increased amount of breakthrough bleeding and thus might lead to reduced adherence to the contraceptive but would not primarily impact contraceptive efficacy. Increased ethinyl estradiol exposure could lead to increased side effects such as breast tenderness, headache, and nausea, which could similarly negatively impact contraceptive adherence. More concerning, increased estrogen exposure is related to estrogen-induced hepatic production of clotting factors and subsequent thrombosis-related complications such as venous thromboembolism, myocardial infarction, and cerebrovascular accident. Moreover, increases in the circulating levels of angiotensinogen with increased estrogen exposure could lead to increases in blood pressure. These effects are largely theoretical, and widespread tolerability concerns due to the effects of ART on increasing hormonal contraceptive levels have not been observed.

12.1 Introduction

Drug–drug interactions (DDIs) are defined as the influence of a second drug on the study drug. The second drug can affect the absorption, distribution, metabolism, and elimination (ADME) of the study drug. The interaction between the drugs can be identified by the change in study drug area under the curve (AUC), maximum plasma concentration (Cmax), time taken to reach maximum plasma concentration (Tmax) by the second drug. In general, the selection of the second drug depends on the same target of disease; sometimes, the target of disease for the treatment of both drugs will differ. In such cases, it will establish the DDI in the clinical trials, the pharmacokinetics of the study drug is also considered. For instance, ketoconazole is the most widely used second drug for understanding the DDI while performing clinical trials (Gorain et al., 2018; Kumar et al., 2021). Severe DDI observed postmarketing led some of the drug manufacturers to withdraw or limit drug use. It was realized that the mechanism behind many DDIs is mediated by the inhibition of metabolism of a drug by the coadministered drugs, which results in increased drug levels showing toxicity (Duke et al., 2012; Zhang et al., 2009).

Thus it has been an essential element for estimating DDI in the drug development stage. Identifying DDI in the early phases of drug development helps avoid the potential adverse drug interactions in postmarketing. Evaluation of DDI is one of the regulatory requirements for new drug application to the United States Food & Drug Administration (USFDA) (Huang et al., 1999).

The DDI can be categorized into two major classes, namely, pharmacokinetic DDI (ADME) and pharmacodynamic DDI (direct effect on receptor function, additive/antagonistic property) (Palleria et al., 2013). Here, in this chapter, author’s primary focus is to discuss the pharmacokinetic-mediated DDI, and therefore, all the proceeding sections will be discussed more elaborately about pharmacokinetic DDI.

The Importance of Recognizing Herb–Drug Interactions

Drug–drug interactions are of great concern for dispensing pharmacists. Fortunately, with the use of drug information systems, drug–drug interactions are easily identified, and typically, pharmacists receive alerts about a known interaction if two interacting drugs exist in a patient’s medication profile. But unfortunately for pharmacists, many pharmacies in the United States are not equipped with the ability to screen for interactions between prescribed western medications and herbal taken at home. According to the 10-year Pharmacist liability report, published by CNA and Healthcare Provider Service Organization in 2013 (HPSO, 2013), only 0.6% of pharmacist closed liability claims were related to drug interaction allegations. The major offenders in pharmacist liability claims were from dispensing the wrong medication (43.8%) or the wrong dose (31.5%). Although the incidences of drug-drug interactions are low, pharmacists should continue to be diligent in screening patients for potential drug drug interactions. At the present time, herb–drug interactions are not subject to protection at a level that is nearly as high as that of drug–drug interactions. In fact, often times, health care providers neglect to ask about the patient’s use of herbal or natural products in the same way that allergy information is asked. More than that, most retail pharmacies in the United States are not well-equipped to protect patients from herb–drug interactions. In order to prevent the occurrence of herb-drug interactions, however, pharmacists must proactively identify patients which are at risk, as many of them assume natural products are safe.

Metabolism

Many drug-drug interactions occur at the level of drug CL, particularly for drugs that undergo metabolism. The CYP450 enzyme system is responsible for the metabolism of many drugs, including antibiotics. These enzymes can be inhibited, induced, or saturated by substrate. For antibiotics that are primarily metabolized by one CYP450 isoform, the potential exists for significant changes in serum concentration, given that CL is either increased or decreased because of changes in enzyme activity. The highest concentration of these enzymes is in the liver, but the intestinal mucosa also contains CYP450 enzymes and is the site of numerous drug-drug interactions that affect bioavailability. To predict a clinically significant drug-drug interaction, therefore, similar questions are asked regarding whether the antibiotic is primarily metabolized by 1 CYP450 isoform or has alternative or parallel metabolic pathways and whether a change in serum concentration would have significance. Specifically, if the serum concentration is lowered, could the antibiotic concentration−time course ratio fall short of the optimal PK-PD–predicted concentration−time course ratio? If the serum concentration is raised, could toxicity occur?

Table 291.5 lists antimicrobial agents that modulate specific CYP450 isoforms that predispose a patient to potential drug-drug interactions when these drugs are coadministered with drugs metabolized by similar pathways. Knowledge of which isoform is primarily responsible for a drug's metabolism provides the clinician with the insight at least to question the possibility of a metabolically based drug-drug interaction. For specific drug-drug interactions, refer to individual agents in Chapter 292.

12.5 Conclusions and Key Points

Drug–drug interactions constitute a significant fraction of avoidable ADRs. The frequency with which they occur depends, in part, on the number of different drugs that are used simultaneously. Since the population is aging, and therefore it is expected that the number of drugs used per person in an aging population will increase, it may well be that the risk of DDIs leading to ADRs will increase sharply. On the other hand, some DDIs can be engineered purposefully to improve dosing schedules or therapeutic outcomes. For the purpose of drug management in general and in an aged population in particular, it is important to be able to identify DDIs that are likely to lead to clinically significant adverse outcomes marked either by ADRs or therapeutic failure. To assist clinicians in identifying risks associated with the combined use of two drugs, drug-interaction books and searchable drug-interaction databases are available. These resources exhibit varying degrees of sophistication and accuracy, and both include some interactions that have never been validated by controlled clinical trials.

Pharmaceutical scientists engaged in drug development require predictive tools to enable them to ascertain whether a drug under development is likely to cause clinically significant drug interactions with marketed drugs. Recognizing the likelihood of clinically significant drug interactions is an important determinant in decisions about continuing further with drug development. Though predictions might be made from analogies to what is known about drug interactions of currently marketed drugs, these analogous predictions can be completely wrong. For example, the anti-infective drug, ciprofloxacin, is a fluoroquinolone and a known inhibitor of CYP1A2. It can sufficiently slow the clearance of theophylline, a drug with a relatively narrow margin of safety, to cause ADRs including seizures. On the other hand, apart from enoxacin, most of the fluoroquinolones that have been marketed since ciprofloxacin do not inhibit CYP1A2, and would not be expected to interact with theophylline. Placing all fluoroquinolones into a probable risk category for an adverse reaction with theophylline based on the findings with ciprofloxacin would lead to bad predictions. Along these lines the macrolide antibiotic, triacetyloleandomycin (TAO), is a reasonably good inhibitor of CYP3A4. Erythromycin and clarithromycin are less effective inhibitors, but have been shown to be capable of causing serious clinical adverse effects associated with CYP3A4 inhibition. On the other hand, neither dirithromycin nor azithromycin, both of which are also macrolide antibiotics, inhibit CYP3A4. They do not share the same risks of DDI-related ADRs as erythromycin, clarithromycin, or TAO.

Beginning at the start of this century an attempt was made to bring together regulatory scientists, those in the pharmaceutical industry, and those in academia to agree upon predictive strategies for characterizing the clinical significance of DDIs. These efforts focused primarily on predicting the clinical significance of inhibitory metabolic and inhibitory transporter-based DDIs, and established the paradigm of using [I]/Ki ratios for this purpose. In this paradigm, DDIs with a high probability of being clinically significant would be those in which the AUC ratio (AUCi/AUC) ≥2, and an [I]/Ki ratio ≥1 would predict an AUC ratio ≥2. Likewise, an AUC ratio ≤1 would suggest that there would be little or no likelihood of a clinically significant DDI, and an [I]/Ki ratio ≤0.1 would predict an AUC ratio ≤1.1. There are many subtleties that must be taken into consideration, however, including the matrix or system in which Ki is estimated, the type of in vitro victim or substrate that is used, nonspecific binding of the victim, the most representative value for the in vivo concentration of [I] for the perpetrator, metabolism of the victim drug by parallel pathways, the issue of cooperativity, and others. Remarkably, even when these subtleties are simply ignored, the predictive accuracy of the [I]/Ki paradigm is between 70 and 85%. The same strategy can be applied to predicting the clinical significance of DDIs associated with transporter inhibition. In view of the relatively high level of predictive accuracy of the [I]/Ki paradigm, flaws notwithstanding, it may not be too soon to begin applying it to the evaluation of DDIs when the only data at hand is case report data. For example, authors of case reports of putative inhibitory DDIs should include [I]/Ki data when it is available, to support any contention of an inhibitory DDI.

Other factors currently limit the predictive accuracy of inhibitory DDIs. For example, although there is some consensus about the drugs that should be used as representative victim drugs of each of the CYP enzymes, such a consensus about representative victim drugs for transporters or for non-CYP enzymes doesn't exist. The exception to this might be for the transporter, P-glycoprotein, for which digoxin may be the victim of choice. By comparison, the study of the function of OATs, OCTs, MRPs, OATPs, and other transporters, their substrates, and their inhibition, is still in its early stages. In addition, some drugs can be substrates for both influx and efflux transporters as well as for CYP or other enzymes. Some drugs can inhibit both CYP enzymes and transporters, for example, PgP, whereas other drugs can induce the activity of CYP enzymes and transporters. The multiplicity of dispositional processes to which a given victim drug might be subject and the multiplicity of processes that a perpetrator drug might affect promise to make our ability to improve on the predictive accuracy of inhibitory DDIs a significant challenge.

Strategies for predicting the clinical significance of enzyme-inducing or transporter-inducing DDIs are not quite as far advanced as for the inhibitory DDIs. To be sure, many of the same issues that limit the predictive accuracy of inhibitory DDIs suffuse predictions for inductive DDIs. However, early work in this field suggests that relatively few perpetrators capable of stimulating, for example, the PXR, will cause clinically meaningful (victim) exposure (AUC) decreases in humans. Moreover, there are currently very few examples of clinical increases in victim clearance that are much greater than two-fold, regardless of the extent of PXR agonism or CYP induction in vitro. Unless a victim has a very narrow therapeutic range, it could be that these types of interactions are simply overrated.

One of the issues that has not been explored extensively in connection with iv/iv predictions of the clinical significance of DDIs is the impact of the victim drug's clearance (before the interaction) on the clinical significance of an interaction in humans. Whether the interaction is one of inhibition of the victim drug's metabolism or whether it is induction of the victim drug's metabolism, even a predicted outcome based, for example, on [I]/Ki ratios might errantly predict real-life clinical consequences if the victim's baseline clearance isn't considered. Remember, it is possible that the pharmacokinetics of a high clearance drug given intravenously will not be substantially affected even in the face of an [I]/Ki ratio of ∼1, although the pharmacokinetics of the same drug given orally might be substantially affected.

Finally, it would appear that most putative protein-binding displacement interactions should not have meaningful clinical consequences, since the AUCunb will not be affected by changes in protein binding. However, there could still be instances in which the displacement of an extensively bound drug could result in both higher peak plasma concentrations of unbound drug and also lower trough plasma concentrations of unbound drug. Detecting the clinical consequences would take very careful clinical observations, since under these circumstances it is possible for such a DDI to cause both an increase in intermittent ADRs and some evidence of intermittent therapeutic failure.

Drug Interactions

Drug-drug interaction (DDI) can occur when two or more drugs are co-administered to a patient. It can lead to changed systemic exposure, resulting in variations in drug response of the co-administered drugs. DDIs generally occur due to inhibition of the metabolism for one drug by the other. It leads to a rise in plasma concentration of the drug whose metabolism is inhibited. The therapeutic index of the drug that has increased concentration in plasma could be decreased, leading to adverse reaction or increase in toxicity. The drug efficacy databases and DDI databases, play crucial roles during concomitant medication and avoid risk of adverse impact on the patient (Duke et al., 2012). Evaluation of potential drug interactions also provides the idea about physiological pathways of its kinetics and dynamics.