Dataset Information

Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine.

ABSTRACT: Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the automated real-time acquisition of pharmacokinetic profiles. Therefore, an online UHPLC-MS/MS system for quantitation of drug concentrations within 3D tumor oral mucosa models was generated. The integration of sampling ports into the 3D tumor models and their culture inside the autosampler allowed for real-time pharmacokinetic profiling without additional sample preparation. Docetaxel quantitation was validated according to EMA guidelines. The tumor models recapitulated the morphology of head-and-neck cancer and the dose-dependent tumor reduction following docetaxel treatment. The administration of four different docetaxel concentrations resulted in comparable courses of concentration versus time curves for 96 h. In conclusion, this proof-of-concept study demonstrated the feasibility of real-time monitoring of drug levels in 3D tumor models without any sample preparation. The inclusion of patient-derived tumor cells into our models may further optimize the pharmacotherapy of cancer patients by efficiently delivering personalized data of the target tissue.

SUBMITTER: Joseph JF

PROVIDER: S-EPMC7284432 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine.

Joseph Jan F JF Gronbach Leonie L García-Miller Jill J Cruz Leticia M LM Wuest Bernhard B Keilholz Ulrich U Zoschke Christian C Parr Maria K MK

Pharmaceutics 20200430 5

Cancer treatment often lacks individual dose adaptation, contributing to insufficient efficacy and severe side effects. Thus, personalized approaches are highly desired. Although various analytical techniques are established to determine drug levels in preclinical models, they are limited in the automated real-time acquisition of pharmacokinetic profiles. Therefore, an online UHPLC-MS/MS system for quantitation of drug concentrations within 3D tumor oral mucosa models was generated. The integrat ...[more]

PMID: 32366029

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Project description:BACKGROUND:The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants. METHODS:We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n =?5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted. RESULTS:Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis. CONCLUSION:In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.

Project description:ABAT deficiency (OMIM 613163) is a rare inborn error of metabolism caused by recessive variants in the gene 4-aminobutyric acid transaminase (ABAT), which is responsible for both the catalysis of GABA and maintenance of nucleoside pools in the mitochondria. To date, only a few patients have been reported worldwide. Their clinical presentation has been remarkably consistent with primary features of severe psychomotor retardation, encephalopathy, hypotonia, and infantile-onset refractory epilepsy. We report a new case of ABAT deficiency that marks an important departure from previous clinical findings. The patient presented at age 6 months with global developmental delay, hypotonia, hypersomnolence and mild choreiform movements. At age 18 months, the subject's clinical presentation was still milder than all previously reported patients and, most notably, did not include seizures. Clinical whole exome sequencing revealed two heterozygous ABAT missense variants that are rare and predicted damaging, but never before reported in a patient and were reported as variants of unknown significance. To test the potential pathogenicity of the variants identified in this patient we developed a cell-based system to test both functions of the ABAT protein via GABA transaminase enzyme activity and mtDNA copy number assays. This systematic approach was validated using vigabatrin, the irreversible inhibitor of ABAT, and leveraged to test the functionality of all ABAT variants in previously reported patients plus the variants in this new case. This work confirmed the novel variants compromised ABAT function to similar levels as variants in previously characterized cases with more severe clinical presentation, thereby confirming the molecular diagnosis of this patient. Additionally, functional studies conducted in cells from both mild and severe patient fibroblasts showed similar levels of compromise in mitochondrial membrane potential, respiratory capacity, ATP production and mtDNA depletion. These results illustrate how cell-based functional studies can aid in the diagnosis of a rare, neurological disorder. Importantly, this patient marks an expansion in the clinical phenotype for ABAT deficiency to a milder presentation that is more commonly seen in pediatric genetics and neurology clinics.

Dataset Information

Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine.

Publications

Automated Real-Time Tumor Pharmacokinetic Profiling in 3D Models: A Novel Approach for Personalized Medicine.

Similar Datasets

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