RESUMO
BACKGROUND: Dosing of chemotherapies is often calculated according to the weight and/or height of the patient or equations derived from these, such as body surface area (BSA). Such calculations fail to capture intra- and interindividual pharmacokinetic variation, which can lead to order of magnitude variations in systemic chemotherapy levels and thus under- or overdosing of patients. METHODS: We designed and developed a closed-loop drug delivery system that can dynamically adjust its infusion rate to the patient to reach and maintain the drug's target concentration, regardless of a patient's pharmacokinetics (PK). FINDINGS: We demonstrate that closed-loop automated drug infusion regulator (CLAUDIA) can control the concentration of 5-fluorouracil (5-FU) in rabbits according to a range of concentration-time profiles (which could be useful in chronomodulated chemotherapy) and over a range of PK conditions that mimic the PK variability observed clinically. In one set of experiments, BSA-based dosing resulted in a concentration 7 times above the target range, while CLAUDIA keeps the concentration of 5-FU in or near the targeted range. Further, we demonstrate that CLAUDIA is cost effective compared to BSA-based dosing. CONCLUSIONS: We anticipate that CLAUDIA could be rapidly translated to the clinic to enable physicians to control the plasma concentration of chemotherapy in their patients. FUNDING: This work was supported by MIT's Karl van Tassel (1925) Career Development Professorship and Department of Mechanical Engineering and the Bridge Project, a partnership between the Koch Institute for Integrative Cancer Research at MIT and the Dana-Farber/Harvard Cancer Center.
RESUMO
Rapid diagnostic tests (RDTs) have shown to be instrumental in healthcare and disease control. However, they have been plagued by many inefficiencies in the laborious empirical development and optimization process for the attainment of clinically relevant sensitivity. While various studies have sought to model paper-based RDTs, most have relied on continuum-based models that are not necessarily applicable to all operation regimes, and have solely focused on predicting the specific interactions between the antigen and binders. It is also unclear how the model predictions may be utilized for optimizing assay performance. Here, we propose a streamlined and simplified model-based framework, only relying on calibration with a minimal experimental dataset, for the acceleration of assay optimization. We show that our models are capable of recapitulating experimental data across different formats and antigen-binder-matrix combinations. By predicting signals due to both specific and background interactions, our facile approach enables the estimation of several pertinent assay performance metrics such as limit-of-detection, sensitivity, signal-to-noise ratio and difference. We believe that our proposed workflow would be a valuable addition to the toolset of any assay developer, regardless of the amount of resources they have in their arsenal, and aid assay optimization at any stage in their assay development process.
