When the combined effects of 2 drugs are equal to the sum of their individual effect it is known as?

Cystic Fibrosis Transmembrane Conductance Regulator Potentiators, Correctors, and Other Treatments for the Basic Defect

On the basis of functional consequences of variousCFTR mutations, specific therapeutic strategies to restore deficient or defective protein activity have been developed by altering CFTR expression or function [Fig. 68.7 andTable 68.4]. Following ambitious high-throughput drug screening efforts to bolster their discovery,206–210 benefits of these new CFTR modulators have rapidly come to fruition and expanded beyond initial indications. Rescue of CFTR protein function to sufficient levels, now termed “highly effective,” as first established by the archetype CFTR modulator ivacaftor, is associated with marked improvement in the clinical outcome that is dramatically more efficacious than previous therapies.189,211,212CFTR potentiators activate CFTR channels located at the cell surface by potentiating cyclic adenosine monophosphate–mediated channel gating.206,213–215 Ivacaftor, a potentiator-type modulator that improves CFTR gating, was the first to be approved and was initially demonstrated in CF patients with the G551D gating mutation, an allele represented in approximately 4% of those with the disease. Subsequently, ivacaftor was also shown to activate 23 additional CFTR alleles as a monotherapy, based on clinical and in vitro data that justified216,217 expansion of its indications. The highly efficacious treatment benefit observed with ivacaftor therapy led to strong interest in recapitulating the effect among other, more commonCFTR alleles.218 This includes correctors of F508del CFTR misfolding, termed CFTR correctors.CFTR correctors restore normal CFTR processing of the most commonCFTR mutation, F508del, and of other alleles with class II properties, and are used in one or two corrector combinations with CFTR potentiators to restore CFTR function. Agents that induce translational readthrough [or suppression] of premature termination codons [class I] to induce expression of full-length CFTR are also under development.219–222CFTR amplifiers augment translation efficiency and thus increase protein levels, which could augment function for various CFTR genotypes. Approaches beyond these small moleculeCFTR modulators are also being advanced. For example, gene replacement by viral and nonviral gene therapy remains an approach under active investigation,223 as well as newer strategies that attempt to express CFTR through translation of delivered mRNA alone.224,225 Because many CF patients [≈40%] are complex heterozygotes and encode more than oneCFTR mutation,226 combination therapeutics addressing more than oneCFTR variant or use of multidrug therapy will likely dictate a need for individualized therapeutics optimized for particular patients in the future.218

Supra-additivity

Supra-additivity or synergism occurs when a mixture of two or more drugs produces a greater response than expected [i.e., greater than the sum of their individual effects; see Figure 16-1]. Drug synergism can be expected when drugs that act by different mechanisms of action are mixed together. Various drug combinations [e.g., nonsteroidal anti-inflammatory drugs [NSAIDs] and opioids; opioids and α2-agonists; opioids and local anesthetics; opioids and dissociative anesthetics] frequently demonstrate synergistic effects. Synergistic drug combinations are the basis for many multimodal drug combinations but must be administered carefully because unwanted and potentially toxic effects may also be potentiated [e.g., respiratory depression and bradyarrhythmias].

Enzyme-Potentiated Desensitization

Enzyme-potentiated desensitization [EPD] is a procedure in which allergen is mixed with the enzyme β-glucuronidase immediately before injection. It is promoted as an improvement over conventional allergen immunotherapy, because it requires fewer injections and uses a lower concentration of allergen. This method was developed by McEwen and colleagues in the 1960s and was studied for several decades.52 A very low dose of allergen, approximately equivalent to the amount administered in a standard skin-prick test, was mixed with partially purified β-glucuronidase in a dose equivalent to the amount of enzyme present in 4 mL of human blood. Immediately after mixing, 0.125 mL of the mixture was injected intradermally. A single preseasonal dose was considered sufficient to produce a therapeutic effect lasting for an entire pollen season,53 and a dose every 2 to 6 months for perennial allergy. Proponents have claimed success in treating allergic rhinitis, asthma, sinusitis, nasal polyposis, eczema, urticaria, migraine headaches, ulcerative colitis, irritable bowel syndrome, rheumatoid arthritis, and petit mal seizures.

The effectiveness of this form of treatment and the presumed effect of β-glucuronidase on the immune system was never established. In a double-blind study in which a single injection was given preseasonally to 44 patients allergic to grass pollen, significant improvement over a placebo effect was evident when measured by overall patient preference and reduced requirement for drug therapy, but daily symptom records showed no effect.53 Several reports of double-blind studies in children with mite-allergic asthma and pollen-allergic rhinitis showed clinical improvement,54 but a similar trial of grass pollen–allergic rhinitis in England showed no treatment effect.55

EPD was based on a notion that the enzyme suppresses the immune response to ambient allergen exposure. The minute amount of enzyme used [far less than what is normally found in situ] is unlikely to have significant pharmacologic effects. It is extremely unlikely that any form of lasting desensitization would be effective with the extremely limited treatment protocol.

Synergistic Treatment Regimens

Synergistic drug combinations have been viewed by many to be a promising approach to treat multiple chronic bacterial diseases [Yin et al., 2014]. Nonetheless, chronic combination treatment regimens can interact in unpredictable ways and show not only drug synergy but also drug antagonism. Drug development aimed at optimizing a synergistic increase in drug efficacy and reducing drug resistance is a highly pursued approach of combinational drug development [Zheng et al., 2018]. Some approaches to suppress the phenotype of multiple drug resistance have included the pairing of antibiotic–antibiotic combinations, adding a nonantibiotic adjuvant molecule to target resistance mechanisms directly or to indirectly target resistance by blocking bacterial signaling pathways [enzymes, and/or two-component systems] [Zheng et al., 2018]. Synergistic drug combinations have the potential to be extremely efficacious by attaining more specific drug targets and reducing the development of drug resistance [Worthington and Melander, 2013].

In recent years, many insights into the process of achieving drug synergy aimed at NTM have been gained, resulting in increasing awareness that this approach can successfully lead to a patient cure [van Ingen et al., 2013a]. Recent preclinical animal models [Orme and Ordway, 2014; Bernut et al., 2017; Obregon-Henao et al., 2015] comparing the antimycobacterial activity of clarithromycin, clofazimine, bedaquiline, and clofazimine–bedaquiline combinations against M. abscessus treatment of GKO and SCID mice demonstrated clofazimine and bedaquiline were the most effective in decreasing the M. abscessus organ burden. This study highlights the importance of synergistic compounds capable of enhanced efficacy against M. abscessus. Additional studies using the zebrafish model demonstrated bedaquiline was highly efficacious against M. abscessus infection and treatment was sufficient to protect the infected larvae from M. abscessus-induced killing [Dupont et al., 2017]. However, clofazimine alone was bacteriostatic for both M. abscessus and M. avium. Clofazimine-amikacin was synergistic against M. abscessus and M. avium [Dupont et al., 2017]. Evaluation of time kill kinetic assays [Ferro et al., 2016] resulted in finding that addition of clofazimine to a regimen of amikacin and clarithromycin led to suppression of the emergence of resistance.

The mechanism of clofazimine and bedaquiline combination treatment synergy has been evaluated in an animal model against drug-resistant strains of M. tuberculosis [Lechartier and Cole, 2015; Shang et al., 2011a]. These studies demonstrated clofazimine appears to act as a prodrug, which is reduced by NADH dehydrogenase [NDH-2], leading to the release of reactive oxygen species upon reoxidation by O2 [Lechartier and Cole, 2015]. Importantly, clofazimine likely competes with menaquinone [MK-4], a main cofactor in the mycobacterial electron transfer chain, for its reduction by NDH-2 [Lechartier and Cole, 2015]. Additional studies investigated the effect of MK-4 supplementation on the action of clofazimine against M. tuberculosis and discovered a direct competition between clofazimine and MK-4 for the cidal effect of clofazimine in nonreplicating and replicating mycobacteria [Lechartier and Cole, 2015]. Additional studies are required to elucidate the synergistic mechanisms of clofazimine and bedaquiline drug combination treatments against NTM.

Preclinical murine models evaluating the efficacy of bedaquiline against M. avium complex species infected mice showed reduced organ bacterial burden; however, this efficacy was not bactericidal [Lounis et al., 2009]. Preclinical studies using bedaquiline for treatment failure of lung disease caused by M. avium [six patients] or M. abscessus [four patients] evaluated bedaquiline treatment between one and eight years [Philley et al., 2015]. Eighty percent of patients enrolled in the study had macrolide-resistant isolate [eight out of ten] [Philley et al., 2015]. All the patients were treated with the same bedaquiline dosage [400 mg daily] and received the best available companion drugs [mean, 5.0 drugs] [Philley et al., 2015]. All patients completed six months of therapy and remain on bedaquiline. After six months of therapy, 60% of patients [six out of ten] had a microbiologic response, with 50% [five out of ten] having one or more negative cultures [Philley et al., 2015]. However, additional preclinical studies evaluating the efficacy of bedaquiline treatment in patients with Mycobacterium intracellulare pulmonary disease showed seven out of 13 patients with an initial culture conversion resulted in relapse [Alexander et al., 2017]. Subsequent sequencing revealed nonsynonymous mmpT5 mutations in isolates from all seven relapse cases, including two pretreatment isolates, supporting the need for further optimization of treatment regimens to prevent the emergence of mmpT5 variants and relapse [Alexander et al., 2017]. In spite of this, bedaquiline and clofazimine combination treatments in the absence of rifamycin may increase drug exposure enhancing efficacy against NTM. Frequently standard treatment for NTM pulmonary disease is not possible because of drug intolerance, antibiotic resistance, or progression of the disease. Second-line treatment such as inhaled amikacin, clofazimine, bedaquiline, and delamanid are considered, regardless of only a few studies to guide their use [Lande et al., 2018].

Potentiation of Inhibitory GABAA and Glycine Receptors

The ether anesthetics [including isoflurane, sevoflurane, and desflurane], the alkane anesthetic halothane, most intravenous anesthetics [including propofol, etomidate, barbiturates], and the neurosteroid anesthetics enhance GABAA and glycine receptor [GlyR] function. GABAA and GlyRs are members of the same cys-loop ligand-gated ion channel superfamily that also includes the cation-permeable nicotinic acetylcholine and 5HT3 receptors. GABAA receptors are the principal transmitter-gated Cl− channels in the neocortex and allocortex, whereas GlyRs fulfill this function in the spinal cord, with some overlap in the diencephalon and brain stem. Activated receptors conduct chloride ions, driving the membrane potential toward the Cl− equilibrium potential. Both receptors are inhibitory [except in some cases during development] because the Cl− equilibrium potential is usually more negative than the normal resting potential. Channel opening also reduces membrane resistance and “shunts” excitatory responses. Most functional GABAA and GlyRs are heteropentamers, typically consisting of three different GABAA subunits [e.g., two α, two β, and one γ or ∂]119 or two different GlyR subunits [three α and two β].120 The subunit composition of GABAA receptors determines their physiologic and pharmacologic properties and varies between and within brain areas as well as between different compartments of individual neurons. Examples are the preferential expression of the α5 subunit in the dendritic field of the hippocampal CA1 area [a region important for memory formation], of the α4 subunit in the thalamus, and of the α6 subunit in the cerebellum. Presence of a γ subunit is required for benzodiazepine modulation of GABAA receptors and can also influence modulation by inhaled anesthetics. Although the molecular mechanisms of receptor modulation by inhaled anesthetics are not clear, these receptors have been key to our understanding of anesthetic-receptor interactions. Using chimeric receptor constructs between anesthetic-sensitive GABAA and insensitive GlyR subunits, specific amino acid residues in transmembrane domains 2 and 3 critical to the action of inhaled anesthetics have been identified.121 This laid the groundwork for the construction of anesthetic-resistant GABAA receptors and the generation of transgenic mice with altered anesthetic sensitivity [discussed later].

The related cation-permeable 5-hydroxytryptamine [serotonin]-3 [5HT3] receptors are similarly potentiated by volatile anesthetics.122,123 5HT3 receptors are involved with autonomic reflexes and also probably contribute to the emetogenic properties of volatile anesthetics.

Unwanted Effects

The most common unwanted side effects are tiredness, drowsiness, and torpor, so-called oversedation. These effects are dose and time related, being most marked within the first 2 h after large doses. Furthermore, drowsiness is most common during the first week of treatment. Both psychomotor skills, such as driving, and intellectual and cognitive skills are affected. As with most depressant drugs, potentiation of the effects of alcohol can occur. Paradoxical behavioral responses may occur in patients taking benzodiazepines. Such events include increased aggression and hostility, acute rage reactions, uncharacteristic criminal behavior such as shoplifting, and uncontrollable weeping. Other unwanted effects include excessive weight gain, skin rash, impairment of sexual function, menstrual irregularities, and, rarely, agranulocytosis.

In pregnant women, benzodiazepines pass readily into the fetus and have been suspected of producing respiratory depression in the neonate. Finally, benzodiazepines are present in the mother's milk and can oversedate the baby. Although overdose with benzodiazepines is extremely common, deaths due to these drugs alone are uncommon.

DRUG SYNERGISM

Many analgesic drugs are additive or synergistic [supraadditive] when administered together. Synergism or supraadditivity implies that the combination of two or more products produces more than additive effects. In more qualitative terms, the combination of two drugs produces a better effect [analgesia] than expected. Drug synergism usually allows the dose of most drugs to be reduced, thereby reducing the potential for side effects. Drug combinations that are likely to be synergistic are produced when drugs that act by different and distinct mechanisms of action are combined.

Synergism or supraadditivity has been demonstrated when local anesthetics are combined with opioids or dissociative anesthetics and when NSAIDs are combined with opioids.

The combination of α2-agonists with opioids produces excellent clinical analgesia in horses [Table 23-4]. The transdermal delivery of opioids [fentanyl patch; 100-μg/h patch per 200 to 250 kg] with low doses of α2-agonists [e.g., xylazine or detomidine] as needed, provides excellent analgesia for extended periods [12 hours]. α2-Agonists are not used systemically to provide analgesia in cattle primarily because of their tendency to cause potent respiratory depression. Cattle are exquisitely sensitive to the respiratory depressant effects of xylazine.

The administration of adjunctive drugs [tranquilizers] in conjunction with major analgesic drugs may potentiate analgesic effects and produces additional calming effects. The simultaneous or sequential administration of acepromazine and meperidine [neuroleptanalgesia] or acepromazine and xylazine potentiates analgesia. Several of the major analgesics [e.g., opioids and α2-agonists] have the added benefit of being reversible [Table 23-4].

2.19.3.2 Dynamic Modeling

Biological systems are not static, but are highly dynamic. Dynamic modeling is, therefore, an essential computational approach in systems chemical biology. It can simulate how a variety of perturbations of a biological system induced by a small molecule affect the behavior of the system. Such simulations are very helpful for deriving mechanistic insights about the system. Discrete logical modeling simplifies biological reality, ignores quantitative kinetic parameters, and, thus, can make qualitative [or semiquantitative] dynamic predictions of system behaviors. Flobak et al.49 developed a dynamical logical model representing a cell fate decision network in the AGS gastric cancer cell line based on knowledge extracted from the literature and databases for discovering drug synergy. With the aid of the software GINsim, the model can cope with the vast state space, simulate the perturbations of the network caused by anticancer drug combinations, and predict the synergistic growth-inhibitory action of some combinations of drugs. In addition, molecular dynamics simulations play an increasingly important role in drug discovery. For example, atomistic dynamic simulations of protein receptors and their small-molecule ligands are very useful in computer-aided drug discovery,50 including virtual compound screening, identification of binding sites [both orthosteric and allosteric], and prediction of binding energies. Okimoto et al.51 conducted a computational screen of compounds by combining molecular docking with massive-scale molecular dynamics simulations. This screening method has a much higher enrichment performance than molecular docking alone. Lastly, when specific kinetic and binding constants are known and extracellular species concentration can be calculated, forward dynamic modeling by ordinary differential equations, stochastic differential equations, or mixed models can be used to determine the combinations of drugs that maximize pathway effects and minimize ADEs.52

Targeted Therapy

Over the past several years, new advances have been made in the development of novel therapeutic agents that target the pathophysiological process at the CFTR chloride channel. In the wake of these findings, CF may no longer be considered a disease with only supportive therapy. Three main categories of new drugs [potentiators, read-through agents, and correctors] have been developed to target the different classes of CFTR gene mutations and are already in phase 2 and phase 3 clinical trials [67,68]. For example, the potentiator category of drug targets class III mutations and is intended to interact with mutant CFTR protein at the apical membrane and enhance the ability of the protein to transport chloride. Likewise, read-through agents target class I mutations by promoting polymerase read-through of nonsense mutations. Finally, correctors act like a pharmacological chaperone and promote trafficking rescue of the mutated CFTR protein.

In 2014, the first mutation targeted drug, Ivacaftor, was approved by the FDA for the treatment of CF in patients 6 years of age or older [21]. Ivacaftor belongs to the potentiator category of CF drugs and works to enhance the ability of the mutant CFTR channel to transport chloride in those patients with class III–V mutations [69]. Specifically, this drug has been shown to be effective in the treatment of patients carrying at least one copy of the p.Gly551Asp [a class III] mutation [70]. Ivacaftor has also been approved for the use in patients who may carry any one of eight additional mutations: p.Gly178Arg, p.Ser549Asn, p.Ser549Arg, p.Gly551Ser, p.Gly1244Glu, p.Ser1251Asn, p.Ser1255Pro, p.Arg117His, and p.Gly1349Asp. It is recommended that CFTR genetic testing be performed to determine a patient’s genotype before therapy is initiated. Recently, the clinical pharmacogenetics implementation consortium published guidelines regarding Ivacaftor therapy and CFTR genotyping [71,72].

3 Computational discovery of drug combinations

A significant challenge of drug repositioning is with demonstrating the efficacy of the identified hits. The drugs identified for new indications in repositioning screens may not be useful in clinical setting due to their weak potency [i.e., low IC50 value] or lack of active compounds [Sun et al., 2016]. Drug combination therapies involving two or three combinations with different mechanism of action overcome this challenge by inhibiting different targets with low concentration of individual drug in combinations [Sun et al., 2016]. For effective drug combination therapies, pre-clinical studies often rely on high-throughput screening where promising candidates are identified based on large collection of chemical compound assays and further investigated for therapy optimization [Xie et al., 2016]. The drug combination effects can be categorized as synergistic, antagonistic, and non-interactive. Synergistic drug combinations will have an effect that is larger than the independent effects of the individual drugs. As a result, drug combinations boost the efficacy and therapeutic options with decreased dosage and delayed drug resistance [Jia et al., 2009]. While antagonistic combinations, in contrast, can have a reduced therapeutic effect when compared to the individual drug effect. This effect is occurred due to the resistance of one of the drugs in the combination therapy. However, an interesting advantage of antagonistic combinations is the ability to invert the selective advantage of single-drug resistant mutants, thereby limiting the expansion of mutations and causing selection against resistance [Chait et al., 2007, 2010; Torella et al., 2010; He et al., 2016]. On other hand, a noninteractive drug combination is still a useful combination for patients having multiple co-morbidities. Drug combination therapies have been pursued in the clinic for treatments of diseases such as HIV [Cihlar and Fordyce, 2016], cardiovascular disease [Huffman et al., 2017], asthma [Saleh, 2008], bacterial infections [Cottarel and Wierzbowski, 2007] and cancer [Mokhtari et al., 2017].

However, it is challenging to screen and identify effective combination since an exhaustive number of combinations need to be experimentally evaluated. Given, n drugs, there will be n[n − 1]/2 possible pairwise drug combinations and the number will exceed for a cocktail of drug combinations. Traditional models such as Bliss, Loewe, HSA and ZIP perform statistical analysis to predict synergistic effect of drug combination. However, to predict potential synergistic drug combination models in a large-scale combination screen setting, these models are ineffective. Recently, several machine learning-based models are proposed for predicting pairwise synergistic drug combinations, providing a hypothesis which subsequently need further validation in vitro or in vivo to confirm and test the efficacy. In Table 2, an overview of computational methods proposed for predicting synergistic drug combination are listed. These proposed models allow integration of heterogenous drug information and CMap transcriptomic data for prediction of optimal combination regimens. Several large-scale rapidly expanding data sources such as DrugBank [Wishart et al., 2018], DCDB [Liu et al., 2014], DrugCombDB [Liu et al., 2020], SYNERGxDB [Seo et al., 2020], CMap [Subramanian et al., 2017] and FDA-approved combinations [Das et al., 2019] avail information particularly for polypharmacy. Of note, many of these datasets curated pairwise interactions and have limited information on higher-order combinations. Hence, predicting higher-order cocktail drug combinations is still a challenging task, yet they are becoming prevalent in clinical setting [Tekin et al., 2017; Meyer et al., 2020]. Yet, some of the past works proposed can handle predicting arbitrary number of drug combinations by limiting number of compounds in the cocktails [Pak et al., 2008; Zinner et al., 2009; Wu et al., 2020; Li et al., 2014; Tonekaboni et al., 2018; Ma and Motsinger-Reif, 2019]. With that being addressed, recent studies suggests that synergy-efficacy tradeoff exists where maximizing synergy may not necessarily maximize efficacy and it is necessary to identify the desired efficacy through constrained optimization of drug concentrations [Gupta and Dixit, 2018; Sen et al., 2019].

Table 2. Computational approaches for synergistic drug combination prediction.

While combination therapy might be a practical extension for drug repositioning in clinical applications, inadvertent drug interactions is a safety concern resulting in alterations of drug pharmacokinetics [PK] and pharmacodynamics [PD]. Often, elderly patients with multiple co-morbidities are prone to polypharmacy and DDIs account for 30% of unexpected adverse reactions leading to morbidity and mortality in United States [Huang et al., 2007; Strandell et al., 2008; Tatonetti et al., 2012b] costing over $177 billion per year in treatments [Ernst and Grizzle, 2001]. Many physicians use CDS systems to prescribe drugs to the patients, which alert contraindications for drug combinations inclusive of pre-prescribed drug combinations and interactions [Chertow, 2001]. However, in a research study conducted in Arizona revealed that physicians overrode two-thirds of the DDI alerts from the CDS systems and it was reported that eight drugs on a study of 113 drugs were responsible for three quarters of important DDI alerts [Slight et al., 2013]. Inadvertent polypharmacy side-effects are challenging to identify in clinical trials due to the impracticality to test all possible combinations [Tatonetti et al., 2012a,b; Bansal et al., 2014]. Hence pharmacovigilance systems such as Food and Drug Administration's Adverse Event Reporting System [FAERS] have been developed for post-market surveillance of drug related adverse events. Healthcare professionals, consumers and manufacturers submit reports to FAERS, which contain adverse event causing drug and de-identified patient information along with the suspected drug and concomitant drugs. Tatonetti et al. developed TWOSIDES, a comprehensive database of putative DDIs mined from FAERS data [Tatonetti et al., 2012a,b]. Few proposed computational algorithms predict the presence of DDI by assuming that drugs with similar features are more likely to interact [Gottlieb et al., 2012; Huang et al., 2013, 2015; Vilar et al., 2013, 2014; Park et al., 2015; Zhang et al., 2015, 2017; Sridhar et al., 2016]. However, predicting the existence of DDI is not sufficient, and hence Zitnik et al. used TWOSIDES database for predicting the potential side-effect of pairwise drug interactions using graph neural network [Zitnik et al., 2018]. Further many such works were proposed to improve the prediction performance of pairwise DDI characterization [Malone et al., 2018; Deac et al., 2019; Ma et al., 2019]. Mondal utilized BERT-based architecture which leverages both SMILES structure and semantic contextual vectors from BioBERT [Lee et al., 2020] for predicting DDI [Mondal, 2020]. Peng et al. proposed deep DDI model [Peng and Ning, 2019] for higher order DDI prediction using FEARS [Chiang et al., 2019] and BMC [Zhang et al., 2017] datasets. Recently, utilization of EHR records has been observed for DDI prediction tasks. GAMENet [Zhang et al., 2020] uses a memory network to embed DDI knowledge graph and EHR data from MIMIC-III [Johnson et al., 2016] using GCN [Kipf and Welling, 2016] to provide safe and personalized recommendation of medication. Similarly, Wang et al. propose a combined order-free medicine prediction network, CompNet, which alleviates unreasonable assumption on the order of the medicines prescribed using graph convolutional reinforcement learning and prescribes personalized medicine [Zhang et al., 2020]. For a bird-eye view level understanding, in Fig. 3, we present a schematic overview of the drug combination discovery pipeline along with the methods.