Electrochemical aptamer-based (EAB) sensors enable the continuous, real-time monitoring of drugs and biomarkers in situ in the blood, brain, and peripheral tissues of live subjects. The real-time concentration information produced by these sensors provides unique opportunities to perform closed-loop, feedback-controlled drug delivery, by which the plasma concentration of a drug can be held constant or made to follow a specific, time-varying profile. Motivated by the observation that the site of action of many drugs is the solid tissues and not the blood, here we experimentally confirm that maintaining constant plasma drug concentrations also produces constant concentrations in the interstitial fluid (ISF). Using an intravenous EAB sensor we performed feedback control over the concentration of doxorubicin, an anthracycline chemotherapeutic, in the plasma of live rats. Using a second sensor placed in the subcutaneous space, we find drug concentrations in the ISF rapidly (30-60 min) match and then accurately (RMS deviation of 8 to 21%) remain at the feedback-controlled plasma concentration, validating the use of feedback-controlled plasma drug concentrations to control drug concentrations in the solid tissues that are the site of drug action. We expanded to pairs of sensors in the ISF, the outputs of the individual sensors track one another with good precision (R 2 = 0.95-0.99), confirming that the performance of in vivo EAB sensors matches that of prior, in vitro validation studies. These observations suggest EAB sensors could prove a powerful new approach to the high-precision personalization of drug dosing.

Background and PurposeThe ability to measure specific molecules at multiple sites within the body simultaneously, and with a time resolution of seconds, could greatly advance our understanding of drug transport and elimination.Experimental ApproachAs a proof‐of‐principle demonstration, here we describe the use of electrochemical aptamer‐based (EAB) sensors to measure transport of the antibiotic vancomycin from the plasma (measured in the jugular vein) to the cerebrospinal fluid (measured in the lateral ventricle) of live rats with temporal resolution of a few seconds.Key ResultsIn our first efforts, we made measurements solely in the ventricle. Doing so we find that, although the collection of hundreds of concentration values over a single drug lifetime enables high‐precision estimates of the parameters describing intracranial transport, due to a mathematical equivalence, the data produce two divergent descriptions of the drug's plasma pharmacokinetics that fit the in‐brain observations equally well. The simultaneous collection of intravenous measurements, however, resolves this ambiguity, enabling high‐precision (typically of ±5 to ±20% at 95% confidence levels) estimates of the key pharmacokinetic parameters describing transport from the blood to the cerebrospinal fluid in individual animals.Conclusions and ImplicationsThe availability of simultaneous, high‐density ‘in‐vein’ (plasma) and ‘in‐brain’ (cerebrospinal fluid) measurements provides unique opportunities to explore the assumptions almost universally employed in earlier compartmental models of drug transport, allowing the quantitative assessment of, for example, the pharmacokinetic effects of physiological processes such as the bulk transport of the drug out of the CNS via the dural venous sinuses.

Electrochemical aptamer-based (EAB) sensors are the first continuous molecular measurement technology that is both (1) able to function in situ in the living body and (2) independent of the chemical reactivity of its targets, rendering it generalizable to a wide range of analytes. Comprised of an electrode-bound, redox-reporter-modified aptamer, signal generation in EAB sensors arises when binding to this target-recognizing aptamer causes a conformation change that, in turn, alters the rate of electron transfer to and from the redox reporter to the electrode surface. A range of electrochemical approaches, including both voltammetric (e.g., cyclic, square wave, and alternating current voltammetry) and non-voltammetric (e.g., chronoamperometry, electrochemical phase interrogation) methods have been used to monitor this change in transfer rate, with square wave voltammetry having dominated recent reports. To date, however, the literature has seen few direct comparisons of the performance of these various approaches. In response we describe here comparisons of EAB sensors interrogated using square wave, differential pulse, and alternating current voltammetry. We find that, while the noise associated with AC voltammetry (in vitro in 37°C whole blood) is exceptionally low, neither this approach nor differential pulse voltammetry support accurate drift correction under these same conditions, suggesting that neither approach is suitable for deployment in vivo. Square wave voltammetry, in contrast, matches or surpasses the gain achieved by the other two approaches, achieves good signal-to-noise, and supports high-accuracy drift correction in 37°C whole blood. Taken together, these results finally confirm that square wave voltammetry is the preferred pulsed voltammetric method for interrogating EAB sensors in complex biological fluids.

Knowledge of drug concentrations in the brains of behaving subjects remains constrained on a number of dimensions, including poor temporal resolution and lack of real-time data. Here, however, we demonstrate the ability of electrochemical aptamer-based sensors to support seconds-resolved, real-time measurements of drug concentrations in the brains of freely moving rats. Specifically, using such sensors, we achieve <4 μM limits of detection and 10-s resolution in the measurement of procaine in the brains of freely moving rats, permitting the determination of the pharmacokinetics and concentration-behavior relations of the drug with high precision for individual subjects. In parallel, we have used closed-loop feedback-controlled drug delivery to hold intracranial procaine levels constant (±10%) for >1.5 hours. These results demonstrate the utility of such sensors in (i) the determination of the site-specific, seconds-resolved neuropharmacokinetics, (ii) enabling the study of individual subject neuropharmacokinetics and concentration-response relations, and (iii) performing high-precision control over intracranial drug levels.

We study the problem of designing an input to a dynamical system that is optimal at estimating unknown parameters in the system’s model. We take the A and D optimality criteria on the Fisher Information Matrix associated with the estimation problem as our optimization objective. Our main motivation is the estimation of the physiological parameters that appear in pharmacokinetic dynamics using a relatively short set of measurements. In this context, model inputs correspond to the intravenous injection of drugs and input selection needs to consider safety constraints that include max-min instantaneous injection rates and total dosage amount. We divide the time interval available for the experiment into learning and optimization stages. We use the initial learning stage to obtain a preliminary estimate for the system’s model. Then we find an optimal input for the optimization stage so that we can improve upon this initial estimate.

Drug action, particularly for centrally-active compounds, is critically dependent upon the transport of molecules across physiological barriers to reach the site of action. The ability to measure specific molecules simultaneously at multiple sites within the body and with seconds time resolution could revolutionize our understanding of drug transport, metabolism, and elimination. It could, for example, improve our understanding of the blood-brain and blood-cerebral spinal fluid barriers that protect the central nervous system by regulating the transfer of molecules to and from these central compartments. As a proof-of-principle demonstration of this, here we describe the use of electrochemical aptamer-based (EAB) sensors to measure transport of the antibiotic vancomycin (a drug that is subject to negligible metabolism or biotransformation) from the plasma to the cerebrospinal fluid of live rats with 7 s temporal resolution. Doing so, we show that, while the collection of hundreds of concentration values over a single drug lifetime enables high-precision estimates of the parameters describing transport, ambiguity is introduced by a mathematical equivalence that produces two divergent pharmacokinetics parameter sets that fit the data equally well. The inclusion of simultaneous, intravenous measurements, however, resolves this equivalence, enabling high-precision (±5 to ±20% at 95% confidence levels) estimates of the pharmacokinetic parameters describing inter-compartmental transport in individual animals. The availability of simultaneous “in-brain” and “in-vein” measurements also provides an opportunity to relax the assumptions almost universally employed in prior compartmental models of drug transport, allowing us to quantitatively address (rather than simply assume), for example, whether the targeted drug is potentially metabolized in brain tissue or actively transported into or out of the ventricles. In sum, the present work highlight the potential of EAB sensors for the tracking intercompartmental molecular transport in the living body, which would not only increase our ability to understand -and therefore modulate- such transport, but could be used as a powerful preclinical tool during drug development to screen drug candidates more effectively in vivo. Such an advance would allow swifter, more accurate, exploration of the clinical impact of novel drug candidates, opening the door for better dosing regimens, as well as streamlining and increasing cost effectiveness of novel drug development.

We address the prediction of the number of new cases and deaths for the coronavirus disease 2019 (COVID-19) over a future horizon from historical data (forecasting). We use a model-based approach based on a stochastic Susceptible-Infections-Removed (SIR) model with time-varying parameters, which captures the evolution of the disease dynamics in response to changes in social behavior, non-pharmaceutical interventions, and testing rates. We show that, in the presence of asymptomatic cases, such model includes internal parameters and states that cannot be uniquely identified solely on the basis of measurements of new cases and deaths, but this does not preclude the construction of reliable forecasts for future values of these measurements. Such forecasts and associated confidence intervals can be computed using an iterative algorithm based on nonlinear optimization solvers, without the need for Monte Carlo sampling. Our results have been validated on an extensive COVID-19 dataset covering the period from March through December 2020 on 144 regions around the globe.

We address the model identification and the computation of optimal vaccination policies for the coronavirus disease 2019 (COVID-19). We consider a stochastic Susceptible– Infected–Removed (SIR) model that captures the effect of multiple vaccine treatments, each requiring a different number of doses and providing different levels of protection against the disease. We show that the inclusion of vaccination data enables the estimation of the state of the model and key model parameters that are otherwise not identifiable. This estimates can, in turn, be used to design strategic approaches to vaccination that aim at minimizing the number of deaths and the economic cost of the disease. We illustrate these results with numerical examples.