Big Data Analyses for Continuous Evaluation of Pharmacotherapy: A Proof of Principle with Doxapram in Preterm Infants

The bioavailability and maturing clearance of doxapram in preterm infants

BACKGROUND

Doxapram is used for the treatment of apnea of prematurity in dosing regimens only based on bodyweight, as pharmacokinetic data are limited. This study describes the pharmacokinetics of doxapram and keto-doxapram in preterm infants.

METHODS

Data (302 samples) from 75 neonates were included with a median (range) gestational age (GA) 25.9 (23.9-29.4) weeks, bodyweight 0.95 (0.48-1.61) kg, and postnatal age (PNA) 17 (1-52) days at the start of continuous treatment. A population pharmacokinetic model was developed using non-linear mixed-effects modelling (NONMEM®).

RESULTS

A two-compartment model best described the pharmacokinetics of doxapram and keto-doxapram. PNA and GA affected the formation clearance of keto-doxapram (CL_{FORMATION KETO-DOXAPRAM}) and clearance of doxapram via other routes (CL_{DOXAPRAM OTHER ROUTES}). For a median individual of 0.95 kg, GA 25.6 weeks, and PNA 29 days, CL_{FORMATION KETO-DOXAPRAM} was 0.115 L/h (relative standard error (RSE) 12%) and CL_{DOXAPRAM OTHER ROUTES} was 0.645 L/h (RSE 9%). Oral bioavailability was estimated at 74% (RSE 10%).

CONCLUSIONS

Dosing of doxapram only based on bodyweight results in the highest exposure in preterm infants with the lowest PNA and GA. Therefore, dosing may need to be adjusted for GA and PNA to minimize the risk of accumulation and adverse events. For switching to oral therapy, a 33% dose increase is required to maintain exposure.

IMPACT

Current dosing regimens of doxapram in preterm infants only based on bodyweight result in the highest exposure in infants with the lowest PNA and GA. Dosing of doxapram may need to be adjusted for GA and PNA to minimize the risk of accumulation and adverse events. Describing the pharmacokinetics of doxapram and its active metabolite keto-doxapram following intravenous and gastroenteral administration enables to include drug exposure to the evaluation of treatment of AOP. The oral bioavailability of doxapram in preterm neonates is 74%, requiring a 33% higher dose via oral than intravenous administration to maintain exposure.

The Pharmacokinetics of Caffeine in Preterm Newborns: No Influence of Doxapram but Important Maturation with Age

BACKGROUND

Apnea of prematurity can persist despite caffeine therapy in preterm infants. Doxapram may additionally support breathing. Although multiple small studies have reported the efficacy of doxapram, the structural co-treatment with caffeine impedes to ascribe the efficacy to doxapram itself or to a pharmacokinetic (PK) interaction where doxapram increases the exposure to caffeine. We examined whether there is a PK drug-drug interaction between doxapram and caffeine by developing a PK model for caffeine including infants with and without doxapram treatment.

METHODS

In preterm neonates receiving caffeine, we determined caffeine plasma concentrations before, during, and directly after doxapram co-treatment and used these to develop a population PK model in NONMEM 7.3. Patient characteristics and concomitant doxapram administration were tested as covariates.

RESULTS

166 plasma samples were collected from 39 preterm neonates receiving caffeine (median gestational age 25.6 [range 24.0-28.0] weeks) of which 65 samples were taken during co-treatment with doxapram (39%, from 32/39 infants). Clearance of caffeine was 9.99 mL/h for a typical preterm neonate with a birth weight of 0.8 kg and 23 days postnatal age and increased with birth weight and postnatal age, resulting in a 4-fold increase in clearance during the first month of life. No PK interaction between caffeine and doxapram was identified.

DISCUSSION

Caffeine clearance is not affected by concomitant doxapram therapy but shows a rapid maturation with postnatal age. As current guidelines do not adjust the caffeine dose with postnatal age, decreased exposure to caffeine might partly explain the need for doxapram therapy after the first week of life.

A Deep Convolutional Neural Network to Improve the Prediction of Protein Secondary Structure

Background:

Protein secondary structure prediction (PSSP) is a fundamental task in bioinformatics that is helpful for understanding the three-dimensional structure and biological function of proteins. Many neural network-based prediction methods have been developed for protein secondary structures. Deep learning and multiple features are two obvious means to improve prediction accuracy.

Objective:

To promote the development of PSSP, a deep convolutional neural network-based method is proposed to predict both the eight-state and three-state of protein secondary structure.

Methods:

In this model, sequence and evolutionary information of proteins are combined as multiple input features after preprocessing. A deep convolutional neural network with no pooling layer and connection layer is then constructed to predict the secondary structure of proteins. L2 regularization, batch normalization, and dropout techniques are employed to avoid over-fitting and obtain better prediction performance, and an improved cross-entropy is used as the loss function.

Results:

Our proposed model can obtain Q3 prediction results of 86.2%, 84.5%, 87.8%, and 84.7%, respectively, on CullPDB, CB513, CASP10 and CASP11 datasets, with corresponding Q8 prediction results of 74.1%, 70.5%, 74.9%, and 71.3%.

Conclusion:

We have proposed the DCNN-SS deep convolutional-network-based PSSP method, and experimental results show that DCNN-SS performs competitively with other methods.

Fused Deposition Modeling (FDM), the new asset for the production of tailored medicines

Over the last few years, conventional medicine has been increasingly moving towards precision medicine. Today, the production of oral pharmaceutical forms tailored to patients is not achievable by traditional industrial means. A promising solution to customize oral drug delivery has been found in the utilization of 3D Printing and in particular Fused Deposition Modeling (FDM). Thus, the aim of this systematic literature review is to provide a synthesis on the production of pharmaceutical solid oral forms using FDM technology. In total, 72 relevant articles have been identified via two well-known scientific databases (PubMed and ScienceDirect). Overall, three different FDM methods have been reported: "Impregnation-FDM", "Hot Melt Extrusion coupled with FDM" and "Print-fill", which yielded to the formulation of thermoplastic polymers used as main component, five families of other excipients playing different functional roles and 47 active ingredients. Solutions are underway to overcome the high printing temperatures, which was the initial brake on to use thermosensitive ingredients with this technology. Also, the moisture sensitivity shown by a large number of prints in preliminary storage studies is highlighted. FDM seems to be especially fitted for the treatment of rare diseases, and particular populations requiring tailored doses or release kinetics. For future use of FDM in clinical trials, an implication of health regulatory agencies would be necessary. Hence, further efforts would likely be oriented to the use of a quality approach such as "Quality by Design" which could facilitate its approval by the authorities, and also be an aid to the development of this technology for manufacturers.

Precision Dosing of Doxapram in Preterm Infants Using Continuous Pharmacodynamic Data and Model-Based Pharmacokinetics: An Illustrative Case Series

INTRODUCTION

Current drug dosing in preterm infants is standardized, mostly based on bodyweight. Still, covariates such as gestational and postnatal age may importantly alter pharmacokinetics and pharmacodynamics. Evaluation of drug therapy in these patients is very difficult because objective pharmacodynamic parameters are generally lacking. By integrating continuous physiological data with model-based drug exposure and data on adverse drug reactions (ADRs), we aimed to show the potential benefit for optimized individual pharmacotherapy.

MATERIALS AND METHODS

Continuous data on oxygen saturation (SpO2), fraction of inspired oxygen (FiO2) and composite parameters, including the SpO2/FiO2 ratio and the cumulative oxygen shortage under the 89% SpO2 limit, served as indicators for doxapram effectiveness. We analyzed these continuous effect data, integrated with doxapram exposure and ADR parameters, obtained in preterm infants around the start of doxapram therapy. The exposures to doxapram and the active metabolite keto-doxapram were simulated using a population pharmacokinetic model. Infants were selected and retrospectively compared on the indication to start doxapram, the first response to doxapram, a potential dose-response relationship, and the administered dosage over time. Recommendations were made for individual improvements of therapy.

RESULTS

We provide eight cases of continuous doxapram administration that illustrate a correct and incorrect indication to start doxapram, responders and non-responders to therapy, and unnecessary over-exposure with ADRs. Recommendations for improvement of therapy include: objective evaluation of added effect of doxapram after start, prevention of overdosing by earlier down-titration or termination of therapy, and the prevention of hypoxia and agitation by measuring specific parameters at strategical time-points.

CONCLUSION

Real-time and non-invasive effect monitoring of drug therapy combined with model-based exposure provides relevant information to clinicians and can importantly improve therapy. The variability between and within patients emphasizes the importance of individual, objective evaluation of pharmacotherapy. These measurements, together with data on ADRs, allow for precision medicine in neonatology that should be brought to the bedside.

Use of Continuous Physiological Monitor Data to Evaluate Doxapram Therapy in Preterm Infants

INTRODUCTION

Evaluation of pharmacotherapy during intensive care treatment is commonly based on subjective, intermittent interpretations of physiological parameters. Real-time visualization and analysis may improve drug effect evaluation. We aimed to evaluate the effects of the respiratory stimulant doxapram objectively in preterm infants using continuous physiological parameters.

METHODS

In this longitudinal observational study, preterm infants who received doxapram therapy were eligible for inclusion. Physiological data (1 Hz) were used to assess respiration and to evaluate therapy effects. The oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio and the area under the 89% SpO2 curve (duration × saturation depth below target) were calculated as measures of hypoxemia. Regression analyses were performed in 1-h timeframes to discriminate therapy failure (intubation or death) from success (no intubation).

RESULTS

Monitor data of 61 patients with a median postmenstrual age (PMA) at doxapram initiation of 28.7 (IQR 27.6-30.0) weeks were available. The success rate of doxapram therapy was 56%. Doxapram pharmacodynamics were reflected in an increased SpO2 and SpO2/FiO2 ratio as well as a decrease in episodes with saturations below target (SpO2 <89%). The SpO2/FiO2 ratio, corrected for PMA and mechanical ventilation before therapy start, discriminated best between therapy failure and success (highest AUC ROC of 0.83).

CONCLUSION

The use of continuous physiological monitor data enables objective and detailed interpretation of doxapram in preterm infants. The SpO2/FiO2 ratio is the best predictive parameter for therapy failure or success. Further implementation of real-time data analysis and treatment algorithms would provide new opportunities to treat newborns.

Simultaneous quantification of fentanyl, sufentanil, cefazolin, doxapram and keto-doxapram in plasma using liquid chromatography-tandem mass spectrometry

A simple and specific UPLC–MS/MS method was developed and validated for simultaneous quantification of fentanyl, sufentanil, cefazolin, doxapram and its active metabolite keto‐doxapram. The internal standard was fentanyl‐d5 for all analytes. Chromatographic separation was achieved with a reversed‐phase Acquity UPLC HSS T3 column with a run‐time of only 5.0 min per injected sample. Gradient elution was performed with a mobile phase consisting of ammonium acetate or formic acid in Milli‐Q ultrapure water or in methanol with a total flow rate of 0.4 mL min⁻¹. A plasma volume of only 50 μL was required to achieve adequate accuracy and precision. Calibration curves of all five analytes were linear. All analytes were stable for at least 48 h in the autosampler. The method was validated according to US Food and Drug Administration guidelines. This method allows quantification of fentanyl, sufentanil, cefazolin, doxapram and keto‐doxapram, which is useful for research as well as therapeutic drug monitoring, if applicable. The strength of this method is the combination of a small sample volume, a short run‐time, a deuterated internal standard, an easy sample preparation method and the ability to simultaneously quantify all analytes in one run.