Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.

Example-based support vector machine for drug concentration analysis

Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

Transscleral Coulomb-controlled iontophoresis of methylprednisolone into the rabbit eye: influence of duration of treatment, current intensity and drug concentration on ocular tissue and fluid levels.

The major problems associated with the use of corticosteroids for the treatment of ocular diseases are their poor intraocular penetration to the posterior segment when administered locally and their secondary side effects when given systemically. To circumvent these problems more efficient methods and techniques of local delivery are being developed. The purposes of this study were: (1) to investigate the pharmacokinetics of intraocular penetration of hemisuccinate methyl prednisolone (HMP) after its delivery using the transscleral Coulomb controlled iontophoresis (CCI) system applied to the eye or after intravenous (i.v.) injection in the rabbit, (2) to test the safety of the CCI system for the treated eyes and (3) to compare the pharmacokinetic profiles of HMP intraocular distribution after CCI delivery to i.v. injection. For each parameter evaluated, six rabbit eyes were used. For the CCI system, two concentrations of HMP (62.5 and 150mg ml(-1)), various intensities of current and duration of treatment were analyzed. In rabbits serving as controls the HMP was infused in the CCI device but without applied electric current. For the i.v. delivery, HMP at 10mg kg(-1)as a 62.5mg ml(-1)solution was used. The rabbits were observed clinically for evidence of ocular toxicity. At various time points after the administration of drug, rabbits were killed and intraocular fluids and tissues were sampled for methylprednisolone (MP) concentrations by high pressure liquid chromatography (HPLC). Histology examinations were performed on six eyes of each group. Among groups that received CCI, the concentrations of MP increased in all ocular tissues and fluids in relation to the intensities of current used (0.4, 1.0 and 2.0mA/0.5cm(2)) and its duration (4 and 10min). Sustained and highest levels of MP were achieved in the choroid and the retina of rabbit eyes treated with the highest current and 10min duration of CCI. No clinical toxicity or histological lesions were observed following CCI. Negligible amounts of MP were found in ocular tissues in the CCI control group without application of current. Compared to i.v. administration, CCI achieved higher and more sustained tissue concentrations with negligible systemic absorption. These data demonstrate that high levels of MP can be safely achieved in intraocular tissues and fluids of the rabbit eye, using CCI. With this system, intraocular tissues levels of MP are higher than those achieved after i.v. injection. Furthermore, if needed, the drug levels achieved with CCI can be modulated as a function of current intensity and duration of treatment. CCI could therefore be used as an alternative method for the delivery of high levels of MP to the intraocular tissues of both the anterior and posterior segments.

Drug concentration response relationships in normal volunteers after oral administration of losartan, an angiotensin II receptor antagonist.

The aim of this study was to investigate the relationships between plasma concentrations of losartan, an orally active angiotensin II inhibitor, its active metabolite EXP3174, and angiotensin II blockade. Six healthy subjects received single oral doses of 40, 80, or 120 mg losartan and placebo at 1-week intervals in a crossover study. Angiotensin II blockade was assessed by the blood pressure response to exogenous angiotensin II before and after losartan administration. EXP3174 reached higher plasma concentrations and was eliminated more slowly than its parent compound; its levels paralleled the profile of angiotensin II blockade closer than losartan. Inhibition of the pressure response was dose dependent. The Hill-shaped relationship between response and EXP3174 concentration (or time-integrated variables) approached a plateau with 80 mg. The dose-dependent increase in plasma renin and angiotensin II exhibited a considerable individual scatter. We conclude that losartan produces a dose-dependent, effective angiotensin II blockade that is largely determined by the active metabolite EXP3174.

Benchmarking therapeutic drug monitoring software: A review of available computer tools

Therapeutic drug monitoring (TDM) aims to optimize treatments by individualizing dosage regimens based on the measurement of blood concentrations. Dosage individualization to maintain concentrations within a target range requires pharmacokinetic and clinical capabilities. Bayesian calculations currently represent the gold standard TDM approach but require computation assistance. In recent decades computer programs have been developed to assist clinicians in this assignment. The aim of this survey was to assess and compare computer tools designed to support TDM clinical activities. The literature and the Internet were searched to identify software. All programs were tested on personal computers. Each program was scored against a standardized grid covering pharmacokinetic relevance, user friendliness, computing aspects, interfacing and storage. A weighting factor was applied to each criterion of the grid to account for its relative importance. To assess the robustness of the software, six representative clinical vignettes were processed through each of them. Altogether, 12 software tools were identified, tested and ranked, representing a comprehensive review of the available software. Numbers of drugs handled by the software vary widely (from two to 180), and eight programs offer users the possibility of adding new drug models based on population pharmacokinetic analyses. Bayesian computation to predict dosage adaptation from blood concentration (a posteriori adjustment) is performed by ten tools, while nine are also able to propose a priori dosage regimens, based only on individual patient covariates such as age, sex and bodyweight. Among those applying Bayesian calculation, MM-USC*PACK© uses the non-parametric approach. The top two programs emerging from this benchmark were MwPharm© and TCIWorks. Most other programs evaluated had good potential while being less sophisticated or less user friendly. Programs vary in complexity and might not fit all healthcare settings. Each software tool must therefore be regarded with respect to the individual needs of hospitals or clinicians. Programs should be easy and fast for routine activities, including for non-experienced users. Computer-assisted TDM is gaining growing interest and should further improve, especially in terms of information system interfacing, user friendliness, data storage capability and report generation.

Monographies on drugs, which are frequently analysed in the course of Therapeutic Drug Monitoring

In 1995 the working group "Drug Monitoring" of the Swiss Society of Clinical Chemistry (SSCC) has already published a printed version of drug monographs, which are now newly compiled and presented in a standardised manner. The aim of these monographs is to give an overview on the most important informations that are necessary in order to request a drug analysis or is helpful to interpret the results. Therefore, the targeted audience are laboratory health professionals or the receivers of the reports. There is information provided on the indication for therapeutic drug monitoring, protein binding, metabolic pathways and enzymes involved, elimination half life time and elimination routes as well as information on therapeutic or toxic concentrations. Because preanalytical considerations are of particular importance for therapeutic drug monitoring, there is also information given at which time the determination of the drug concentration is reasonable and when steady-state concentrations are reached after changing the dose. Furthermore, the stability of the drug and its metabolite(s), respectively, after blood sampling is described. For readers with a specific interest, references to important publications are given. The number of the monographs will be continuously enlarged. The updated files are presented on the homepage of the SSCC (www.sscc.ch).

A drug administration decision support system

Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.

Drug uptake in a rodent sarcoma model after intravenous injection or isolated lung perfusion of free/liposomal doxorubicin.

The distribution of free and liposomal doxorubicin (Liporubicin) administered by intravenous injection (IV) or isolated lung perfusion (ILP) was compared in normal and tumor tissues of sarcoma bearing rodent lungs. A single sarcomatous tumor was generated in the left lung of 35 Fischer rats, followed 10 days later by left-sided ILP (n=20) or IV drug administration (n=12), using 100 microg and 400 microg free or liposomal doxorubicin, respectively. The tumor and lung tissue drug concentration was measured by HPLC. Free doxorubicin administered by ILP resulted in a three-fold (100 microg) and 10-fold (400 microg) increase of the drug concentration in the tumor and normal lung tissue compared to IV administration. In contrast, ILP with Liporubicin resulted in a similar drug uptake in the tumor and lung tissue compared to IV injection. For both drug formulations and dosages, ILP resulted in a higher tumor to lung tissue drug ratio but also in a higher spatial heterogeneity of drug distribution within the lung compared to IV administration. ILP resulted in a higher tumor to lung tissue drug ratio and in a more heterogeneous drug distribution within the lung compared to IV drug administration.

Orosomucoid (alpha1-acid glycoprotein) plasma concentration and genetic variants: effects on human immunodeficiency virus protease inhibitor clearance and cellular accumulation.

BACKGROUND AND OBJECTIVE: Protease inhibitors are highly bound to orosomucoid (ORM) (alpha1-acid glycoprotein), an acute-phase plasma protein encoded by 2 polymorphic genes, which may modulate their disposition. Our objective was to determine the influence of ORM concentration and phenotype on indinavir, lopinavir, and nelfinavir apparent clearance (CL(app)) and cellular accumulation. Efavirenz, mainly bound to albumin, was included as a control drug. METHODS: Plasma and cells samples were collected from 434 human immunodeficiency virus-infected patients. Total plasma and cellular drug concentrations and ORM concentrations and phenotypes were determined. RESULTS: Indinavir CL(app) was strongly influenced by ORM concentration (n = 36) (r2 = 0.47 [P = .00004]), particularly in the presence of ritonavir (r2 = 0.54 [P = .004]). Lopinavir CL(app) was weakly influenced by ORM concentration (n = 81) (r2 = 0.18 [P = .0001]). For both drugs, the ORM1 S variant concentration mainly explained this influence (r2 = 0.55 [P = .00004] and r2 = 0.23 [P = .0002], respectively). Indinavir CL(app) was significantly higher in F1F1 individuals than in F1S and SS patients (41.3, 23.4, and 10.3 L/h [P = .0004] without ritonavir and 21.1, 13.2, and 10.1 L/h [P = .05] with ritonavir, respectively). Lopinavir cellular exposure was not influenced by ORM abundance and phenotype. Finally, ORM concentration or phenotype did not influence nelfinavir (n = 153) or efavirenz (n = 198) pharmacokinetics. CONCLUSION: ORM concentration and phenotype modulate indinavir pharmacokinetics and, to a lesser extent, lopinavir pharmacokinetics but without influencing their cellular exposure. This confounding influence of ORM should be taken into account for appropriate interpretation of therapeutic drug monitoring results. Further studies are needed to investigate whether the measure of unbound drug plasma concentration gives more meaningful information than total drug concentration for indinavir and lopinavir.

Monitoring drug therapy.

Drug development has improved over recent decades, with refinements in analytical techniques, population pharmacokinetic-pharmacodynamic (PK-PD) modelling and simulation, and new biomarkers of efficacy and tolerability. Yet this progress has not yielded improvements in individualization of treatment and monitoring, owing to various obstacles: monitoring is complex and demanding, many monitoring procedures have been instituted without critical assessment of the underlying evidence and rationale, controlled clinical trials are sparse, monitoring procedures are poorly validated and both drug manufacturers and regulatory authorities take insufficient account of the importance of monitoring. Drug concentration and effect data should be increasingly collected, analyzed, aggregated and disseminated in forms suitable for prescribers, along with efficient monitoring tools and evidence-based recommendations regarding their best use. PK-PD observations should be collected for both novel and established critical drugs and applied to observational data, in order to establish whether monitoring would be suitable. Methods for aggregating PK-PD data in systematic reviews should be devised. Observational and intervention studies to evaluate monitoring procedures are needed. Miniaturized monitoring tests for delivery at the point of care should be developed and harnessed to closed-loop regulated drug delivery systems. Intelligent devices would enable unprecedented precision in the application of critical treatments, i.e. those with life-saving efficacy, narrow therapeutic margins and high interpatient variability. Pharmaceutical companies, regulatory agencies and academic clinical pharmacologists share the responsibility of leading such developments, in order to ensure that patients obtain the greatest benefit and suffer the least harm from their medicines.

Improvement of therapeutic drug monitoring of imatinib by Bayesian prediction of trough levels. Personalized drug dosage

Aims: Plasma concentrations of imatinib differ largely between patients despite same dosage, owing to large inter-individual variability in pharmacokinetic (PK) parameters. As the drug concentration at the end of the dosage interval (Cmin) correlates with treatment response and tolerability, monitoring of Cmin is suggested for therapeutic drug monitoring (TDM) of imatinib. Due to logistic difficulties, random sampling during the dosage interval is however often performed in clinical practice, thus rendering the respective results not informative regarding Cmin values.Objectives: (I) To extrapolate randomly measured imatinib concentrations to more informative Cmin using classical Bayesian forecasting. (II) To extend the classical Bayesian method to account for correlation between PK parameters. (III) To evaluate the predictive performance of both methods.Methods: 31 paired blood samples (random and trough levels) were obtained from 19 cancer patients under imatinib. Two Bayesian maximum a posteriori (MAP) methods were implemented: (A) a classical method ignoring correlation between PK parameters, and (B) an extended one accounting for correlation. Both methods were applied to estimate individual PK parameters, conditional on random observations and covariate-adjusted priors from a population PK model. The PK parameter estimates were used to calculate trough levels. Relative prediction errors (PE) were analyzed to evaluate accuracy (one-sample t-test) and to compare precision between the methods (F-test to compare variances).Results: Both Bayesian MAP methods allowed non-biased predictions of individual Cmin compared to observations: (A) - 7% mean PE (CI95% - 18 to 4 %, p = 0.15) and (B) - 4% mean PE (CI95% - 18 to 10 %, p = 0.69). Relative standard deviations of actual observations from predictions were 22% (A) and 30% (B), i.e. comparable to the intraindividual variability reported. Precision was not improved by taking into account correlation between PK parameters (p = 0.22).Conclusion: Clinical interpretation of randomly measured imatinib concentrations can be assisted by Bayesian extrapolation to maximum likelihood Cmin. Classical Bayesian estimation can be applied for TDM without the need to include correlation between PK parameters. Both methods could be adapted in the future to evaluate other individual pharmacokinetic measures correlated to clinical outcomes, such as area under the curve(AUC).

Personalized drug administrations using Support Vector Machine

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients' health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor's clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).