Greco Model Research Articles

Combination Therapy with UV-4B and Molnupiravir Enhances SARS-CoV-2 Suppression.

The host targeting antiviral, UV-4B, and the RNA polymerase inhibitor, molnupiravir, are two orally available, broad-spectrum antivirals that have demonstrated potent activity against SARS-CoV-2 as monotherapy. In this work, we evaluated the effectiveness of UV-4B and EIDD-1931 (molnupiravir's main circulating metabolite) combination regimens against the SARS-CoV-2 beta, delta, and omicron BA.2 variants in a human lung cell line. Infected ACE2 transfected A549 (ACE2-A549) cells were treated with UV-4B and EIDD-1931 both as monotherapy and in combination. Viral supernatant was sampled on day three when viral titers peaked in the no-treatment control arm, and levels of infectious virus were measured by plaque assay. The drug-drug effect interaction between UV-4B and EIDD-1931 was also defined using the Greco Universal Response Surface Approach (URSA) model. Antiviral evaluations demonstrated that treatment with UV-4B plus EIDD-1931 enhanced antiviral activity against all three variants relative to monotherapy. These results were in accordance with those obtained from the Greco model, as these identified the interaction between UV-4B and EIDD-1931 as additive against the beta and omicron variants and synergistic against the delta variant. Our findings highlight the anti-SARS-CoV-2 potential of UV-4B and EIDD-1931 combination regimens, and present combination therapy as a promising therapeutic strategy against SARS-CoV-2.

Response surface model comparison and combinations for remifentanil and propofol in describing response to esophageal instrumentation and adverse respiratory events

The primary aim of this essay was to explore the best fitting model in remifentanil-propofol combined administrations during esophageal instrumentation (EI) from five distinct response surface models. The secondary aim was to combine the models to give appropriate effect-site drug concentrations (Ces) range with maximal comfort and safety. The Greco, reduced Greco, Minto, Scaled C50 Hierarchy and Fixed C50 Hierarchy models were constructed to fit four drug effects: loss of response to esophageal instrumentation (LREI), loss of response to esophageal instrumentation revised (LREIR), intolerable ventilatory depression (IVD) and respiratory compromise (RC). Models were tested by chi-square statistical test and evaluated with Akaike Information Criterion (AIC). Model prediction performance were measured by successful prediction rate (SPR) and three prediction errors. The reduced Greco model was the best fitting model for LREI and RC, and the Minto model was the best fitting model for LREIR and IVD. The SPRs of reduced Greco model for LREI and RC were 81.76% and 79.81%. The SPRs of Minto model for LREIR and IVD were 80.32% and 80.12%. Overlay of the reduced Greco model for LREI and Minto model for IVD offered visual aid for guidance in drug administration. Using proper response surface model to fit different drug effects will describe the interactions between anesthetic drugs better. Combining response surface models to select the more reliable effect-site drug concentrations range can be used to guide clinical drug administration with greater safety and provide an improvement of anesthesia precision.

Thermodynamic Interpretation of a Machine-Learning-Based Response Surface Model and Its Application to Pharmacodynamic Synergy between Propofol and Opioids

Propofol and fentanyl are commonly used agents for the induction of anesthesia, and are often associated with hemodynamic disturbances. Understanding pharmacodynamic impacts is vital for parasympathetic and sympathetic tones during the anesthesia induction period. Inspired by the thermodynamic interaction between drug concentrations and effects, we established a machine-learning-based response surface model (MLRSM) to address this predicament. Then, we investigated and modeled the biomedical phenomena in the autonomic nervous system. Our study prospectively enrolled 60 patients, and the participants were assigned to two groups randomly and equally. Group 1 received propofol first, followed by fentanyl, and the drug sequence followed an inverse procedure in Group 2. Then, we extracted and analyzed the spectrograms of electrocardiography (ECG) and pulse photoplethysmography (PPG) signals after induction of propofol and fentanyl. Eventually, we utilized the proposed MLRSM to evaluate the relationship between anesthetics and the integrity/balance of sympathetic and parasympathetic activity by employing the power of high-frequency (HF) and low-frequency (LF) bands and PPG amplitude (PPGA). It is worth emphasizing that the proposed MLRSM exhibits a similar mathematical form to the conventional Greco model, but with better computational performance. Furthermore, the MLRSM has a theoretical foundation and flexibility for arbitrary numbers of drug combinations. The modeling results are consistent with the previous literature. We employed the bootstrap algorithm to inspect the results’ consistency and measure the various statistical fluctuations. Then, the comparison between the modeling and the bootstrapping results was used to validate the statistical stability and the feasibility of the proposed MLRSM.

Propofol pharmacokinetic and pharmacodynamic profile and its electroencephalographic interaction with remifentanil in children.

Propofol and remifentanil are commonly combined during total intravenous anesthesia. The impact of remifentanil in this relationship is poorly quantified in children. Derivation of an integrated pharmacokinetic and pharmacodynamic propofol model, containing remifentanil pharmacodynamic interaction information, enables propofol effect-site target-controlled infusion in children with a better prediction of its hypnotic effect when both drugs are combined. We designed this study to derive an integrated propofol pharmacokinetic-pharmacodynamic model in children and to describe the pharmacodynamic interaction between propofol and remifentanil on the electroencephalographic bispectral index effect. Thirty children (mean age: 5.45years, range 1.3-11.9; mean weight: 23.5kg, range 8.5-61) scheduled for elective surgery with general anesthesia were studied. After sevoflurane induction, maintenance of anesthesia was based on propofol and remifentanil. Blood samples to measure propofol concentration were collected during anesthesia maintenance and up to 6hours in the postoperative period. Bispectral index data were continuously recorded throughout the study. A pharmacokinetic-pharmacodynamic model was developed using population modeling. The Greco model was used to examine the pharmacokinetic-pharmacodynamic interaction between propofol and remifentanil for BIS response RESULTS: Propofol pharmacokinetic data from a previous study in 53 children were pooled with current data and simultaneously analyzed. Propofol pharmacokinetics were adequately described by a three-compartment distribution model with first-order elimination. Theory-based allometric relationships based on TBW improved the model fit. The Greco model supported an additive interaction between propofol and remifentanil. Remifentanil showed only a minor effect in BIS response. We have developed an integrated propofol pharmacokinetic-pharmacodynamic model that can describe the pharmacodynamic interaction between propofol and remifentanil for BIS response. An additive interaction was supported by our modeling analysis.

Comparisons of Analysis Methods for Assessment of Pharmacodynamic Interactions Including Design Recommendations

Quantitative evaluation of potential pharmacodynamic (PD) interactions is important in tuberculosis drug development in order to optimize Phase 2b drug selection and ultimately to define clinical combination regimens. In this work, we used simulations to (1) evaluate different analysis methods for detecting PD interactions between two hypothetical anti-tubercular drugs in in vitro time-kill experiments, and (2) provide design recommendations for evaluation of PD interactions. The model used for all simulations was the Multistate Tuberculosis Pharmacometric (MTP) model linked to the General Pharmacodynamic Interaction (GPDI) model. Simulated data were re-estimated using the MTP–GPDI model implemented in Bliss Independence or Loewe Additivity, or using a conventional model such as an Empirical Bliss Independence-based model or the Greco model based on Loewe Additivity. The GPDI model correctly characterized different PD interactions (antagonism, synergism, or asymmetric interaction), regardless of the underlying additivity criterion. The commonly used conventional models were not able to characterize asymmetric PD interactions, i.e., concentration-dependent synergism and antagonism. An optimized experimental design was developed that correctly identified interactions in ≥ 94% of the evaluated scenarios using the MTP–GPDI model approach. The MTP–GPDI model approach was proved to provide advantages to other conventional models for assessing PD interactions of anti-tubercular drugs and provides key information for selection of drug combinations for Phase 2b evaluation.

An overview of pharmacodynamic drug interaction with isobole and response surface model in anesthesia

Drug interaction is the principal concept of anesthetic practice. Typically, drug interactions are divided into three categories i.e., additive, synergistic, or infra-additive interactions. Pharmacodynamic drug interactions are typically described using mathematical models. The traditional model is an isobole, which is an iso-effect curve that shows dose or concentration combinations that result in equal effect. Response surface model is a pharmacodynamic tool that describes all isoboles and concentration effect curves for a given endpoint in one equation. In summarizing concentration-effect relationships, the response surface model allows anesthesiologists the versatility to work with precise and safe drug interactions. The aim of this review is to provide the reader with principal concepts of the isobole and response surface model and evaluate characteristics of most commonly used models, including 4 response surface models i.e., the Greco model, reduced Greco model, Minto model, and the Hierarchy model. In addition, the concept of population analysis using nonlinear mixed effects modeling is introduced. Keywords: Drug interactions; Pharmacology

Synergism between rocuronium and cisatracurium: comparison of the Minto and Greco interaction models

BackgroundThis study was conducted to investigate the pharmacodynamic interaction between rocuronium and cisatracurium using the response surface model, which is not subject to the limitations of traditional isobolographic analysis.MethodsOne hundred and twenty patients were randomly allocated to receive one of the fifteen predefined combinations of rocuronium and cisatracurium. To study single drugs, cisatracurium 0.2, 0.15, or 0.1 mg/kg or rocuronium 0.8, 0.6 or 0.4 mg/kg doses were administered alone. To study the pharmacodynamic interaction, drugs were applied in three types of combination ratio, i.e., half dose of each drug alone, 75% of each single dose of rocuronium and 25% of each single dose of cisatracurium, and vice versa. Train-of-four (TOF) ratio and T1% (first twitch of the TOF presented as percentage compared to the initial T1) were used as pharmacodynamic endpoints, and the Greco and Minto models were used as surface interaction models.ResultsThe interaction term α of the Greco model for TOF ratio and T1% measurements showed synergism with values of 0.977 and 1.12, respectively. Application of the Minto model resulted in U50 (θ) values (normalized unit of concentration that produces 50% of the maximal effect in the 0 < θ < 1 region) less than 1 for both TOF ratio and T1% measurements, indicating that rocuronium and cisatracurium exhibit synergism.ConclusionsResponse surface modeling of the interaction between rocuronium and cisatracurium, based on considerations of their effects on muscle relaxation as measured by TOF ratio and T1%, indicated that the two drugs show considerable synergism.

Pharmacodynamics of liposomal amphotericin B and flucytosine for cryptococcal meningoencephalitis: safe and effective regimens for immunocompromised patients.

Cryptococcal meningoencephalitis is a lethal infection with relatively few therapeutic options. The optimal dosage of liposomal amphotericin B (LAmB) alone or in combination with flucytosine is not known. A murine model of cryptococcal meningoencephalitis was used. The fungal density in the brain was determined using quantitative cultures. Pharmacokinetic-pharmacodynamic relationships were determined for LAmB and flucytosine administered alone. The effect of the combination was described using the Greco model and a mathematical model. The results were bridged to humans. Inoculation resulted in hematogenous dissemination and logarithmic growth within the central nervous system. There was histological evidence of multifocal infection throughout the brain. Both LAmB and flucytosine produced a dose-dependent reduction in fungal burden. The effect of the combination of agents in the brain was additive. Bridging studies suggested that a human dosage of LAmB 3 mg/kg/d resulted in a submaximal antifungal effect. Regimens of LAmB 6 mg/kg/d alone, LAmB 3 mg/kg/d plus flucytosine 50 mg/kg/d, and LAmB 3 mg/kg/d plus flucytosine 100 mg/kg/d all resulted in near-maximal antifungal activity. Potential regimens for further study in clinical trials include LAmB 6 mg/kg/d alone, LAmB 3 mg/kg/d plus flucytosine 50 mg/kg/d, and LAmB 3 mg/kg/d plus flucytosine 100 mg/kg/d.

Combination of Voriconazole and Anidulafungin for Treatment of Triazole-Resistant Aspergillus fumigatus in an In Vitro Model of Invasive Pulmonary Aspergillosis

Voriconazole is a first-line agent for the treatment of invasive pulmonary aspergillosis. Isolates with elevated voriconazole MICs are increasingly being seen, and the optimal treatment regimen is not defined. We investigated whether the combination of voriconazole with anidulafungin may be beneficial for the treatment of A. fumigatus strains with elevated voriconazole MICs. We used an in vitro model of the human alveolus to define the exposure-response relationships for a wild-type strain (voriconazole MIC, 0.5 mg/liter) and strains with defined molecular mechanisms of triazole resistance (MICs, 4 to 16 mg/liter). All strains had anidulafungin minimum effective concentrations (MECs) of 0.0078 mg/liter. Exposure-response relationships were estimated using galactomannan as a biomarker. Concentrations of voriconazole and anidulafungin were measured using high-performance liquid chromatography (HPLC). The interaction of voriconazole and anidulafungin was described using the Greco model. Fungal growth was progressively inhibited with higher drug exposures of voriconazole. Strains with elevated voriconazole MICs required proportionally greater voriconazole exposures to achieve a comparable antifungal effect. Galactomannan concentrations were only marginally reduced by anidulafungin monotherapy. An additive effect between voriconazole and anidulafungin was apparent. In conclusion, the addition of anidulafungin does not markedly alter the exposure-response relationship of voriconazole. A rise in serum galactomannan during combination therapy with voriconazole and anidulafungin should be interpreted as treatment failure and not attributed to a paradoxical reaction related to echinocandin treatment.

Sevoflurane Remifentanil Interaction

Various pharmacodynamic response surface models have been developed to quantitatively describe the relationship between two or more drug concentrations with their combined clinical effect. We examined the interaction of remifentanil and sevoflurane on the probability of tolerance to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy in patients to compare the performance of five different response surface models. Forty patients preoperatively received different combined concentrations of remifentanil (0-12 ng/ml) and sevoflurane (0.5-3.5 vol.%) according to a criss-cross design (160 concentration pairs, four per patient). After having reached pseudosteady state, the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy was recorded. For the analysis of the probability of tolerance, five different interaction models were tested: Greco, Reduced Greco, Minto, Scaled C50(O) Hierarchical, and Fixed C50(O) Hierarchical model. All calculations were performed with NONMEM VI (Icon Development Solutions, Ellicott City, MD). The pharmacodynamic interaction between sevoflurane and remifentanil was strongly synergistic for both the hypnotic and the analgesic components of anesthesia. The Greco model did not result in plausible parameter estimates. The Fixed C50(O) Hierarchical model performed slightly better than the Scaled C50(O) Hierarchical and Reduced Greco models, whereas the Minto model fitted less well. We showed the importance of exploring various surface model approaches when studying drug interactions. The Fixed C50(O) Hierarchical model fits our data on sevoflurane remifentanil interaction best and appears to be an appropriate model for use in hypnotic-opioid drug interaction.

Drug interaction: focusing on response surface models

Anesthesiologists have been aware of the importance of optimal drug combination long ago and performed many investigations about the combined use of anesthetic agents. There are 3 classes of drug interaction: additive, synergistic, and antagonistic. These definitions of drug interaction suggest that a zero interaction model should exist to be used as a reference in classifying the interaction of drug combinations. The Loewe additivity has been used as a universal reference model for classifying drug interaction. Most anesthetic drugs follow the sigmoid Emax model (Hill equation); this model will be used for modeling response surface. Among lots of models for drug interaction in the anesthetic area, the Greco model, Machado model, Plummer model, Carter model, Minto model, Fidler model, and Kong model are adequate to be applied to the data of anesthetic drug interaction. A model with a single interaction parameter does not accept an inconsistency in the classes of drug interactions. To solve this problem, some researchers proposed parametric models which have a polynomial interaction function to capture synergy, additivity, and antagonism scattered all over the surface of drug combinations. Inference about truth must be based on an optimal approximating model. Akaike information criterion (AIC) is the most popular approach to choosing the best model among the aforementioned models. Whatever the good qualities of a chosen model, it is uncertain whether the chosen model is the best model. A more robust inference can be extracted from averaging several models that are considered relevant.

Response Surface Modeling of the Interaction between Propofol and Sevoflurane

Propofol and sevoflurane display additivity for gamma-aminobutyric acid receptor activation, loss of consciousness, and tolerance of skin incision. Information about their interaction regarding electroencephalographic suppression is unavailable. This study examined this interaction as well as the interaction on the probability of tolerance of shake and shout and three noxious stimulations by using a response surface methodology. Sixty patients preoperatively received different combined concentrations of propofol (0-12 microg/ml) and sevoflurane (0-3.5 vol.%) according to a crisscross design (274 concentration pairs, 3 to 6 per patient). After having reached pseudo-steady state, the authors recorded bispectral index, state and response entropy and the response to shake and shout, tetanic stimulation, laryngeal mask airway insertion, and laryngoscopy. For the analysis of the probability of tolerance by logistic regression, a Greco interaction model was used. For the separate analysis of bispectral index, state and response entropy suppression, a fractional Emax Greco model was used. All calculations were performed with NONMEM V (GloboMax LLC, Hanover, MD). Additivity was found for all endpoints, the Ce(50, PROP)/Ce(50, SEVO) for bispectral index suppression was 3.68 microg. ml(-1)/ 1.53 vol.%, for tolerance of shake and shout 2.34 microg . ml(-1)/ 1.03 vol.%, tetanic stimulation 5.34 microg . ml(-1)/ 2.11 vol.%, laryngeal mask airway insertion 5.92 microg. ml(-1) / 2.55 vol.%, and laryngoscopy 6.55 microg. ml(-1)/2.83 vol.%. For both electroencephalographic suppression and tolerance to stimulation, the interaction of propofol and sevoflurane was identified as additive. The response surface data can be used for more rational dose finding in case of sequential and coadministration of propofol and sevoflurane.

Potential activity of paclitaxel, vinorelbine and gemcitabine in anaplastic thyroid carcinoma

Anaplastic thyroid carcinoma (ATC) has a rapidly fatal course in the mostly elderly patients with a median survival after diagnosis of 4-12 months. Activity of commonly used chemotherapy (doxorubicin) is low, thus more active compounds need to be introduced into the therapeutic concept of ATC. Recently, based on preclinical data Ain et al. conducted a clinical phase II study with paclitaxel 96 h infusion in ATC achieving a promising response rate of 53%. To further improve therapeutic options in ATC, we evaluated the activity of topotecan, oxaliplatin, vinorelbine, gemcitabine and paclitaxel in comparision to cisplatin and doxorubicin (1 and 96 h drug exposure) alone or in combination in the ATC cell lines SW1736 and 8505C. IC50 values were determined by the sulforhodamine B assay, potential clinical activity was estimated by relative antitumor activity (RAA) and drug interaction was analyzed using a parametric response surface approach (Greco model) of the Loewe additivity. Duration of drug effect was estimated by regrowth kinetics. We found paclitaxel, vinorelbine and gemcitabine active in ATC with RAA (1 h drug exposure) ranging from 86 to 454, 15 to 17 and 31 to 140, respectively. The activity of doxorubicin and cisplatin was moderate with RAA ranging from 1.4 to 2.2 and 0.2 to 0.3, respectively. Combined drug exposure of gemcitabine/paclitaxel and gemcitabine/vinorelbine was synergistic with a Loewe index > 0. However, these results did not reach statistical significance with p > 0.05. At clinically relevant drug concentrations paclitaxel, gemcitabine and vinorelbine but not oxaliplatin exerted a sustained growth inhibition after cessation of drug exposure for the complete assay time of 15 days. In conclusion, paclitaxel, gemcitabine and vinorelbine but not topotecan or oxaliplatin appeared to be active in anaplastic thyroid carcinoma based on RAA or growth delay at clinically relevant drug concentrations. Combinations of vinorelbine/gemcitabine and paclitaxel/gemcitabine exerted a trend to synergy. Thus, further evaluation of paclitaxel, vinorelbine and gemcitabine alone or in combination with ATC seems warranted.

Surface Response Modeling to Examine the Combination of Amphotericin B Deoxycholate and 5‐Fluorocytosine for Treatment of Invasive Candidiasis

The strategy of combining antifungal drugs in a treatment regimen may improve the outcome of invasive candidiasis. Using a well-validated pharmacodynamic murine model of invasive candidiasis, we defined the effect of the combination of amphotericin B deoxycholate (AmB) and 5-fluorocytosine (5FC) by use of the Greco model of drug interaction. The combination was additive, meaning that the experimental effect did not deviate in a statistically significant manner from the null reference model (or additive surface) of the combined effect. From a clinical perspective, the addition of 5FC to a regimen of AmB may enable the near-maximum effect to be reached in circumstances in which the administration of a given dose of AmB alone produces a submaximum effect but an increase in the dose is not possible, because of dose-related toxicity. Our methods provide a way in which some of the complex issues surrounding antifungal combination treatment can be addressed.

Assessingin vitrocombinations of antifungal drugs against yeasts and filamentous fungi: comparison of different drug interaction models

Non-parametric and parametric approaches of two competing zero-interaction theories--the Loewe additivity and the Bliss independence - were evaluated for analyzing the in vitro interactions of various antifungal drugs. Fifty-one data sets, derived from three drug combinations, tested in triplicate against 17 clinical yeast and mold isolates with a two-dimensional checkerboard microdilution technique, were selected to span from strong synergy to strong antagonism. These were analyzed with the standard FIC index model and modern concentration-effect response surface models: the fully parametric model developed by Greco et al. and the 3-D analysis developed by Prichard et al. The FIC index model is subjective, sensitive to experimental errors and resulted in approximated results and variable conclusions depending on the MIC endpoints determined and interpretation endpoints used. By using the MIC-2 endpoint (lowest drug concentration showing 50% of growth) for calculating the FIC indices, problems due to trailing phenomena were reduced and weak interactions could be detected; higher levels of reproducibility and agreement with the other models were achieved using the MIC-0 and MIC-1 (lowest drug concentration showing 10 and 25% of growth, respectively). High reproducibility was achieved in interpreting the FIC indices when the cutoffs of 0.25 and 4 (for single experiments) and the cutoff of 1 (for replicates) were used for defining the limits of additivity/indifference. Although the fully parametric Greco model did not describe precisely the entire response surface of all antifungal drug interactions, it was able to differentiate synergistic from non-synergistic interactions with a non-unit, reproducible, concentration-independent interaction parameter, including its uncertainty, without requiring replication. The Bliss independence based models resulted in mosaics of synergistic and antagonistic combinations, raising questions about the concentration-dependent nature of antifungal drug interaction. The sum of all statistically significant interactions were used as a summary interaction parameter for the entire response surface, concluding synergy or antagonism when it was positive or negative, respectively. The cutoffs of 100% and 200% were used to distinguish weak and moderate interactions, respectively in 12-16 x 8-12 checkerboard formats. Semi-parametric approaches need particular care as experimental errors are not eliminated from the entire response surface.

Potent synergistic in vitro interaction between nonantimicrobial membrane-active compounds and itraconazole against clinical isolates of Aspergillus fumigatus resistant to itraconazole.

To develop new approaches for the treatment of invasive infections caused by Aspergillus fumigatus, the in vitro interactions between itraconazole (ITZ) and seven different nonantimicrobial membrane-active compounds--amiodarone (AMD), amiloride, lidocaine, lansoprazole (LAN), nifedipine (NIF), verapamil, and fluphenazine--against seven ITZ-susceptible and seven ITZ-resistant (ITZ-R) strains were evaluated by the checkerboard microdilution method based on National Committee for Clinical Laboratory Standards M-38A guidelines. The nature and the intensity of the interactions were assessed by a nonparametric approach (fractional inhibitory concentration [FIC] index model), a fully parametric response surface approach (Greco model) of the Loewe additivity no-interaction theory, and the nonparametric (Prichard model) and semiparametric response surface approaches of the Bliss independence (BI) no-interaction theory. Statistically significant synergy was found for the combination of ITZ and AMD and the combination of LAN and NIF, although with different intensities against ITZ-R strains. The FIC index values ranged from 1 to 0.02 for ITZ-AMD, 0.53 to 0.04 for ITZ-LAN, and 0.28 to 0.06 for ITZ-NIF. By use of the BI-based model, the strongest synergy was found for the combination of ITZ with AMD, followed by the combination of ITZ and NIF. The parametric models could not be fit adequately because most of the drugs alone did not show any effect and, thus, no sigmoid dose-response. In general, the combination of ITZ with calcium pump blockers displayed in vitro synergistic activity, primarily against ITZ-R strains, and warrants further investigation.

In vitro drug interaction modeling of combinations of azoles with terbinafine against clinical Scedosporium prolificans isolates.

The in vitro interaction between terbinafine and the azoles voriconazole, miconazole, and itraconazole against five clinical Scedosporium prolificans isolates after 48 and 72 h of incubation was tested by a microdilution checkerboard (eight-by-twelve) technique. The antifungal effects of the drugs alone and in combination on the fungal biomass as well as on the metabolic activity of fungi were measured using a spectrophotometric method and two colorimetric methods, based on the lowest drug concentrations showed 75 and 50% growth inhibition (MIC-1 and MIC-2, respectively). The nature and the intensity of the interactions were assessed using a nonparametric approach (fractional inhibitory concentration [FIC] index model) and a fully parametric response surface approach (Greco model) of the Loewe additivity (LA) no-interaction theory as well as a nonparametric (Prichard model) and a semiparametric response surface approaches of the Bliss independence (BI) no-interaction theory. Statistically significant synergy was found between each of the three azoles and terbinafine in all cases, although with different intensities. A 27- to 64-fold and 16- to 90-fold reduction of the geometric mean of the azole and terbinafine MICs, respectively, was observed when they were combined, resulting in FIC indices of <1 to 0.02. Using the MIC-1 higher levels of synergy were obtained, which were more consistent between the two incubation periods than using the MIC-2. The strongest synergy among the azoles was found with miconazole using the BI-based models and with voriconazole using the LA-based models. The synergistic effects both on fungal growth and metabolic activity were more potent after 72 h of incubation. Fully parametric approaches in combination with the modified colorimetric method might prove useful for testing the in vitro interaction of antifungal drugs against filamentous fungi.