Cardiotoxicity Induced by Antifungal Drugs

Allergic Bronchopulmonary Aspergillosis (ABPA) in the Era of Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) Modulators

Allergic bronchopulmonary aspergillosis (ABPA) is a hypersensitivity disease caused by Aspergillus fumigatus (Af), prevalent in persons with cystic fibrosis (CF) or asthma. In ABPA, Af proteases drive a T-helper cell-2 (Th2)-mediated allergic immune response leading to inflammation that contributes to permanent lung damage. Corticosteroids and antifungals are the mainstays of therapies for ABPA. However, their long-term use has negative sequelae. The treatment of patients with CF (pwCF) has been revolutionized by the efficacy of cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapy. Pharmacological improvement in CFTR function with highly effective elexacaftor/tezacaftor/ivacaftor (ETI) provides unprecedented improvements in lung function and other clinical outcomes of pwCF. The mechanism behind the improvement in patient outcomes is a continued topic of investigation as our understanding of the role of CFTR function evolves. As ETI therapy gains traction in CF management, understanding its potential impact on ABPA, especially on the allergic immune response pathways and Af infection becomes increasingly crucial for optimizing patient outcomes. This literature review aims to examine the extent of these findings and expand our understanding of the already published research focusing on the intersection between ABPA therapeutic approaches in CF and the rapid impact of the evolving CFTR modulator landscape. While our literature search yielded limited reports specifically focusing on the role of CFTR modulator therapy on CF-ABPA, findings from epidemiologic and retrospective studies suggest the potential for CFTR modulator therapies to positively influence pulmonary outcomes by addressing the underlying pathophysiology of CF-ABPA, especially by decreasing inflammatory response and Af colonization. Thus, this review highlights the promising scope of CFTR modulator therapy in decreasing the overall prevalence and incidence of CF-ABPA.

A Physiologically Based Pharmacokinetic Model of Ketoconazole and Its Metabolites as Drug-Drug Interaction Perpetrators

The antifungal ketoconazole, which is mainly used for dermal infections and treatment of Cushing's syndrome, is prone to drug-food interactions (DFIs) and is well known for its strong drug-drug interaction (DDI) potential. Some of ketoconazole's potent inhibitory activity can be attributed to its metabolites that predominantly accumulate in the liver. This work aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model of ketoconazole and its metabolites for fasted and fed states and to investigate the impact of ketoconazole's metabolites on its DDI potential. The parent-metabolites model was developed with PK-Sim^® and MoBi^® using 53 plasma concentration-time profiles. With 7 out of 7 (7/7) DFI AUC_last and DFI C_max ratios within two-fold of observed ratios, the developed model demonstrated good predictive performance under fasted and fed conditions. DDI scenarios that included either the parent alone or with its metabolites were simulated and evaluated for the victim drugs alfentanil, alprazolam, midazolam, triazolam, and digoxin. DDI scenarios that included all metabolites as reversible inhibitors of CYP3A4 and P-gp performed best: 26/27 of DDI AUC_last and 21/21 DDI C_max ratios were within two-fold of observed ratios, while DDI models that simulated only ketoconazole as the perpetrator underperformed: 12/27 DDI AUC_last and 18/21 DDI C_max ratios were within the success limits.

Deriving waveform parameters from calcium transients in human iPSC-derived cardiomyocytes to predict cardiac activity with machine learning

Human induced pluripotent stem cell-derived cardiomyocytes have been established to detect dynamic calcium transients by fast kinetic fluorescence assays that provide insights into specific aspects of clinical cardiac activity. However, the precise derivation and use of waveform parameters to predict cardiac activity merit deeper investigation. In this study, we derived, evaluated, and applied 38 waveform parameters in a novel Python framework, including (among others) peak frequency, peak amplitude, peak widths, and a novel parameter, shoulder-tail ratio. We then trained a random forest model to predict cardiac activity based on the 25 parameters selected by correlation analysis. The area under the curve (AUC) obtained for leave-one-compound-out cross-validation was 0.86, thereby replicating the predictions of conventional methods and outperforming fingerprint-based methods by a large margin. This work demonstrates that machine learning is able to automate the assessment of cardiovascular liability from waveform data, reducing any risk of user-to-user variability and bias.

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An open-source algorithm was developed to derive parameters from waveform data

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A machine learning model was trained to predict cardiac activity of compounds

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Three parameters for peak width, height, and shape were found to be most predictive

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The model can facilitate the assessment of cardiovascular liability