EP2035985A1

EP2035985A1 - Device and method for calculating and supplying a drug dose

Info

Publication number: EP2035985A1
Application number: EP07764693A
Authority: EP
Inventors: Stefan Willmann; Karsten HÖHN; Andrea Edginton; Walter Schmitt
Original assignee: Bayer Technology Services GmbH
Current assignee: Bayer Intellectual Property GmbH
Priority date: 2006-06-20
Filing date: 2007-06-16
Publication date: 2009-03-18
Also published as: DE102006028232A1; WO2007147539A1; CA2660618A1; US20090306944A1

Abstract

The invention relates to a device for use in the clinical/therapeutical field for the patient-individual optimization of the dosage and/or the dosage scheme of a drug based on rational, mathematical models which take into consideration possible physiological variations that are due to the illness or other particularities of the patient and the interaction with co-drugs that are administered at times close to each other. The invention also relates to the supply of said drug dose by means of a dosage device.

Description

Apparatus and method for calculating and providing a dose of medicament

The present invention relates to a device for use in the clinical / therapeutic area for patient-individual optimization of the dosage and / or dosage regimen of a drug based on rational, mathematical models, which disease-related physiological changes and other peculiarities of the patient and the interaction with take into account co-medications administered promptly, and the provision of this drug dosage by means of a dosing device.

It is well known that a number of different individual factors of the patient can affect the uptake, distribution, and excretion of a drug (the so-called pharmacokinetics) and thus also its temporal profile (the pharmacodynamics). The most important influencing factors include the weight, age or gender of the patient and the functioning of his excretory organs such as liver or kidney. The need for a patient-specific dosage is often given in clinical-therapeutic practice, but is usually implemented only inadequate. Special patient populations such. For example, children or old patients may require appropriate dosages due to age or disease-related differences in the uptake, distribution and excretion of drugs to ensure the safety and efficacy of drug therapy.

Patient-specific dosages are particularly problematical if the need for dose adjustment results from the interaction with one or more further medicaments which is administered promptly to the same patient (co-medication). In such a case, which is often given in clinical practice, there is the possibility of mutual interference, for. When two substances are degraded via the same metabolic pathway in the liver or are substrates of the same transporter protein. For example, processes such as induction or inhibition of enzymes may cause the dose to be changed and adjusted during therapy.

The reasons for non-patient-administered dosing of drugs in clinical practice are manifold. For solid, perorally administered dosage forms such as tablets or capsules, which are preferred over other forms of administration such as intravenous drug administration in clinical use, the availability of easy-to-dose drugs is often not given. In the case of tablets or capsules, it is usually only possible to vary the dose by taking several tablets or capsules or by dividing the tablet. The possibility of a true individualization of the drug dose is so severely limited. Liquid formulations are still relatively easy to dose by dimensioning a defined volume of fluid, e.g. B. by means of a measuring cup or dropper. However, only relatively few drugs in liquid form are available on the market.

Another important reason for non-patient-specific drug dosing is the lack of time in the clinical therapeutic environment. The time that a clinician has in the clinical routine to select a dosage per patient is on average only a few minutes. If dose adjustment is required, the doctor will take information about the dosage to be administered, if any, from the instruction leaflet or literature in the form of books, reports or tables, which usually takes a considerable amount of time. In addition, tabulated dosing information is unable to accommodate specific factors of the individual patient. The small amount of time available to the practitioner to address the issue of optimized dosage also poses a risk of dosage errors that are not uncommon in everyday clinical practice.

US 2002/0091546 describes a computer-based method which administers clinically / therapeutically relevant information such as patient data as well as data on indications and active substances and makes it available to the practitioner as a decision-making aid in the dosing and therapy planning.

US 2001/0001144 discloses a computer-based method for therapy management, which calculates a drug dosage to the pharmacist or practitioner from patient data and data about the drug to be administered. This paper also describes the consideration of interactions with other medications. To determine the patient-specific dosage, the patient data of the patient to be treated are compared with the data of previous patients with similar characteristics and clinical picture (patient data matching).

A disadvantage of the two methods described in US 2002/0091546 and US 2001/0001144 is that the information used is evidence-based, i. H. mainly based on empirical knowledge such. B. develop published case studies. Therefore, both methods are severely limited in the safety of their application to those patients who have sufficient similarity and comparability to already known cases.

Software for managing the workflow in a hospital pharmacy is also known in the art and commercially available. Examples of this include the AutoMed's Efficiency WorkPath ™ system from AmerisourceBergen Technology Group, PKon Pharmacy Management System from SRS Systems or the Pharmacy Management Software from RX-Link. However, such existing software products do not yet offer the possibility of considering and dynamically describing patient-individual properties on the anthropometric / physiological, pathophysiological, biochemical or genetic level as well as the time-dependent interaction with co-drugs in the individual dose calculation.

In order to make valid predictions about how the concentration-time profile and the drug-time profile of a drug in an individual patient looks like, without having to resort to comparable case studies, complex physiology-based simulation models are required. Such simulation models are known from the literature and described in detail, for example, in WO2005 / 033982 for mammalian organisms (including the human organism). Methods which determine individually optimized drug dosages using such simulation models in combination with physiological, anatomical and genetic information of the patient to be treated are described in WO2005 / 33334, WO2005 / 684731, WO2005 / 116854 and DE 10 2005 028 080.

A first important prerequisite for the reliable prediction of the pharmacokinetics or pharmacodynamics of a drug is an accurate estimate of the elimination rates (the so-called clearance) for the drug of each patient. These metabolic and excretion rates can vary widely from person to person, e.g. Due to different factors such as age, gender, race, the presence of pathophysiological factors such as renal or hepatic insufficiency or individual genetic differences. For example, the variability of the activity of the enzymes of the cytochrome CYP450 system may be known to be a cause that the effects and side effects of a drug at the same dosage may vary greatly from patient to patient. For several enzymes of this class, genetic polymorphisms are known that significantly reduce or completely eliminate the activity. In children and the elderly, the age-dependent activity of individual enzymes and transporter proteins must also be taken into account. Methods for scaling clearance in a child based on the clearance of an adult with knowledge of the involved degradation and elimination processes and their age-dependent activity are known in the literature [AN Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach for the Scaling of Clearance in Children ", Clin. Pharmacokin. (accepted for publication 2005), S. Bjorkman: "Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modeling: theophylline and midazolam as model drugs." Br J Clin Pharmacol. 2005 Jun; 59 (6): 691-704.]. Meanwhile, biological test methods (so-called gene chips) are available with which the activities of certain pharmacokinetically relevant enzymes can be determined experimentally.

In special subpopulations such as renal or hepatic insufflation patients, a scaling of excretion rates may occur, for example. For example, clinical parameters such as creatinine clearance or the status of liver enzymes.

The second important requirement is a correct scaling of the volume of distribution, which essentially results from substance properties such as lipophilicity and free fraction in the plasma as well as the individual body composition (water, fat and protein content), which is also dependent on the age and condition of the patient , Certain diseases, eg. For example, those associated with malnutrition or poor use of ingested food alter the composition of the body in terms of water, fat and protein content. This is known in the art.

Apparatuses for measuring and providing a patient-specific dose of medicament are known and commercially available. Examples of such so-called. Unit Dose Systems are the Cadet ^® from AccuChart ™ systems, medical packaging system of Medical Packaging Inc., Swisslog's PillPicker systems or the AUTOMED ^® system of AmerisourceBergen Technology Group and others. DE 103 09 473 likewise describes a device for producing an individual solid medicament dosage. However, this patent does not disclose by which method the optimal dose for each patient is determined.

However, methods and devices that adequately address time-dependent processes such as enzyme induction or inhibition in dosing detection are not known in the art.

It is an object of the present invention to develop a safe and rapid point-of-care device which determines a dose of medication optimally tailored to the individual patient and then provides this promptly to the treating person or the patient. When determining the patient-specific dose of medication, relevant parameters of the patient of anthropometric / physiological, pathophysiological, biochemical and / or genetic nature should be taken into account as well as information and parameters specific to the drug to be administered. If additional medication is administered to the patient during treatment, pharmacokinetic and dynamic effects of co-medication should also be considered. The device according to the invention should continue to take into account the interaction in the dose calculation and at Incompatibility of drug combinations and existing contraindications additionally issue a warning to the practitioner. In addition to optimizing the dosage, ie the amount to be administered, the device according to the invention should be able to eliminate any undesired side effects due to drug interactions through an optimized dosage scheme, ie by specifying time intervals between the administration of the medicaments involved; to minimize. The provision of the drug in the optimal dosage should be promptly at the point of care, ie, for example, in the hospital or in the doctor's office.

These objects are achieved by the device according to the invention. The solution to the problem posed is provided by a device comprising an input unit (1), a calculation unit (2) and an associated automatic drug delivery device (3) (Figure 1).

The input unit (1) serves to acquire the relevant individual patient information (101) of the patient to be treated.

The individual parameters of the patient include, for example, two parameters: body weight (BW), height (H) or body mass index (BMI = BW / H ² ). It also includes age, gender, race, sizes that together with the body weight allow a statement about the body composition such. Lean Body Mass (LBM), Fat Free Body Mass (FFBM) or Total Body Fat Mass (TBFM). Furthermore, this includes genetic information such. As expression or activity of metabolizing enzymes or transporter proteins, information about the functioning of the excretory organs such as kidney and liver, or information on existing allergies or food or drug intolerances.

In the case of co-medication, the dosage regimens of all other medications administered are also relevant and are therefore among the input parameters. In addition to co-medications in the true sense of the word, food ingredients can also affect the pharmacokinetics of drugs, resulting in similar adverse interactions with drugs. This is z. As the St. John's wort or green tea and grapefruit juice the case. Such food ingredients are then treated analogously to the co-drugs.

The input unit is all common data input systems for computers. Particularly preferred for use in the clinic or in the doctor's office is a handheld device. The input of individual parameters of the patient to be treated (101) is usually carried out by the practitioner. Furthermore, it is conceivable that the required patient-specific information on a portable computer-readable storage medium, for. As a smart card, are stored, and read via a reader or are already in the form of a digital medical record the attending physician available.

The calculation unit (2) calculates the optimal drug dose and, if appropriate, the optimal dosage regimen. It consists of computer-implemented software and the hardware needed to run the program. The hardware is usually a standard PC. This is either directly connected to the input device, as in the case of a laptop computer with built-in keyboard or chip card reader, or installed in a remote and connected to the input device (server). In principle, all common transmission techniques, both wired and wireless methods are suitable and conceivable. Particularly preferred is a wireless transmission of the information entered via the handheld input module or the smart card reader patient information.

The software manages all information relevant to the calculation of the optimal dosage of medication in one or more databases as well as the calculation of the patient-specific dose. These information relevant to the calculation of the drug dose can be subdivided into physiological (or anthropometric) information (201), pathological information (202), drug-specific information (203) and optionally information on additionally administered medicaments, so-called co-drugs (204 ).

Relevant physiological or anthropometric (201) and pathophysiological information (202) are analogous to the individual patient information (101), for example age, sex, race, body weight, body size, body mass index, lean body mass, fat free body mass, gene expression data, diseases , Allergies, medication, kidney function and liver function.

Relevant pathophysiological information (202) are in particular diseases, allergies, renal function and liver function

The drug information (203) includes, for example, lipophilicity, free plasma fraction, blood plasma ratio, volume of distribution, clearance, type of clearance, clearance proportions, type of excretion, dosage regimen, transporter substrate, PD endpoint, and side effects. Relevant drug information (203) is in particular the recommended therapeutic dosage (according to the manufacturer), pharmacodynamic endpoint, clearance (total clearance as blood or plasma clearance in a reference population or a reference individual) and type of clearance (hepatic metabolically, biliary, renal, etc.). ) as well as the shares of the individual processes in the total clearance, kinetic parameters of active transporters, if the drug substrate for one or more active transporters, as well as physicochemical and pharmacokinetic information such. Lipophilicity, unbound plasma fraction, plasma proteins to which the drug binds, blood / plasma partition coefficient, or volume of distribution.

In the case of co-medications, the corresponding above-mentioned information on all other drugs administered is content of the co-medication database (204).

Empirical knowledge, which z. B. can be obtained by the search of case studies, may also be additional part of the databases with drug information or information on co-drugs.

The calculation of the optimal dose and, if appropriate, the optimal dosage regimen will be based on individual patient data using a rational mathematical model to calculate the pharmacokinetic and pharmacodynamic behavior of the drug to be administered based on the information contained in the databases (205). Rational mathematical models in this context may be, for example, allometric scaling functions or physiology-based pharmacokinetic models.

In a preferred embodiment of the invention, a physiologically-based pharmacokinetic / pharmacodynamic simulation model is used to calculate the individual dosage. Particularly preferred is the dynamically generated physiologically-based simulation model, which is described in detail in WO2005 / 633982.

A particular advantage of using the physiology-based simulation model from WO2005 / 633982 lies in the possibility of simulating a simultaneous administration of several drugs and their interaction dynamically. Dynamic in this context means that in the interaction, the kinetics of the two (possibly also of several) interacting substances can be taken into account. This is compared to a stati- view, in which z. B. an enzyme or a transporter is completely or partially inhibited without time dependence, of advantage, since the dynamic simulation allows an optimization of the dosing schema. One possible result of such an optimization of the dosing mas is, for example, a maximum time interval of z. 12 hours (once a day) to administer two interacting substances to minimize mutual interference.

Processes such as enzyme inhibition or induction are known to be time-dependent, so that interaction effects based on these processes are also time-dependent. In special cases, these dynamic effects, which take place on a time scale of several days or weeks, may necessitate a dose adjustment of a drug in the course of therapy. A simple static consideration, or merely the issuing of a warning to the practitioner with timely administration of interacting drugs, as known in the art, does not do justice to such complex, dynamic effects.

In summary, the dynamically coupled simulation models described in WO2005 / 633982 form the basis for the optimization of the dosing scheme. By means of an iterative method, for example, the influence of the time delay of administration of the drug and the co-drug interacting therewith can be simulated on the desired pharmacodynamic and undesired side effects, thus optimizing the parallel administration of both drugs.

During operation of the device according to the invention, the optimum dose of medication obtained for the patient in question is transmitted to the automatic dosing device (3). The location of the automatic dosing device (3) is not particularly limited, and may be the hospital pharmacy in the case of a hospital, for example. The transmission of the information of the optimal dose of medication to the automatic dosing device (3) can be wired or wireless, or even electronically stored / transmitted as a recipe or transmitted in paper form. In the automatic dosing device, the drug dose is measured (301) according to the conventional methods known and provided after preparation to the treating person or patient (302). Suitable automatic dosing devices for volumetric or gravimetric measurement of liquids are in liquid formulations, in the case of solid dosage forms known in the prior art unit-dose systems.

In a particularly simple embodiment, the patient information (101) merely consists of specifying the age and weight of the patient, in particular the indication of the age and weight of a child. As further physiological / anthropometric parameters (201), mean values for a child of the corresponding age are assumed, pathological changes conditions are disregarded in the simplest embodiment. The dose calculation is done by scaling the clearance according to one of the methods described in the literature from the value for adults, such. See, for example, AN Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children," Clin. Pharmacokin. (accepted for publication 2005) (see also Example A).

The particular advantages of the inventive device, which can be used as a point of care solution depending on the version directly in the clinic or doctor's office, are in the time savings for the practitioner and in the significantly reduced error rate. Both aspects make a significant contribution to making medication therapy safer and more efficient. In addition, the device according to the invention can advantageously be integrated into existing software solutions that manage the workflow in a hospital pharmacy. A further advantage of the device according to the invention and of the underlying method is that for the first time it is possible to adequately take into account the interactions occurring in the case of co-medication and to thereby enable a quantity and temporally optimized parallel administration of several medicaments adapted to the respective situation.

The invention is illustrated by the following examples without, however, limiting them to these examples.

Examples

Em essential aspect of the device according to the invention is the calculation of an optimal drug dosage taking into account individual factors and parameters of the patient to be treated. The following examples show how these factors and parameters affect pharmacokinetics and demonstrate the validity of the physiology-based pharmacokinetic simulation. The examples are based on simulations with the physiology-based pharmacokinetic model PK-Sim ^® (Version 3.0) developed by Bayer Technology Services. Two of the examples relate to the substance ciprofloxacin, but this is not to be understood as a restriction to this substance or to substances of the same substance class.

A: Calculation of drug dosage in children

The calculation of a drug dosage in children is therapeutically highly relevant. To date, the vast majority of all medications are only approved for use in adults as there is no information on the effects and side effects in children. The pediatrician is faced with the dilemma of having a highly effective drug at his disposal but not being allowed to use it in the Kmd. In doing so, he has to weigh up whether the non-licensed use or the withholding of the drug therapy with the appropriate drug harms the patient more.

Frequently, drugs are then administered without authorization ("unhcensed") or outside of the approved indications ("off-label") to the child with sometimes serious consequences. The infant organism differs greatly from the adult organism in its composition of water, protein and fat and in the activity of the excretory organs (especially liver and kidney), which inevitably results in pharmacokinetic and pharmacodynamic differences. For an exact dose adjustment it is necessary to consider both the age-related differences in body composition, which particularly affect the distribution volume, as well as the activity of the excretory organs, which leads to age-related clearance of the clearance. The age-related nature of clearance is more important in terms of dose adjustment, as clearance in a Kmd may vary by more than an order of magnitude, depending on the age and process of excretion. In this example, a combined method is used that scales age-dependent clearance based on the value of an adult to the prospective value in a child. This method uses two approaches known from the literature. One approach is the allometric scaling of clearance based on the child's body weight using an allometric equation [Anderson BJ, Meakin GH. scaling for size: some implications for paediatπc anesthesia dosmg. Paediatr Anesth 2002; 12 (3): 205-219, Holford NH. A size standard for pharmacokmetics. Clin Pharmacokinet 1996; 30 (5): 329-332]:

CL - CL x TBW ^cluld f- ⁷⁵

V ^{ß W} adult y

Where CL _ch , i _{d is} the clearance of the area, CL _adu _{t is} the clearance in the adult (both in non-normalized flow units such as ml / mm), BW _chlld the body weight of the child concerned and BW _adu i _t the body weight of the adult (which is usually set at 70 kg). This allometric approach requires in addition to the reference data of the adult as the only input the body weight of the child to be treated. The disadvantage of this allo- metric approach is that the same intrinsic activities of the excretory processes in adults and in the Kmd are assumed; the differences between child and adult are attributed solely to the size difference. In particular, in newborns and infants but z. B. the metabolizing enzymes in the liver or the Ehminationssys- tem in the kidney is not fully developed. This enzyme ontogenesis is process-dependent, ie the different liver enzymes reach the activity level of an adult at different times. The literature describes mechanistic models describing the age-dependent development of different processes of neonatal to adult initiation. Even clearance values of preterm infants, who again represent a special subpopulation in terms of liver and kidney maturation, can be predicted with such mechanistic models [AN Edgmton, W. Schmitt, B. Voith, S. Willmann: "A Mechamstic Approach for the Scahng of Clearance in Children, "Clin. Pharmacokin. 2006, 45 (7) 683-704]. However, in contrast to the simple allometric approach, the mechanistic scaling of clearance requires, in addition to a clearance reference value in adults, precise knowledge of the proportions of all the involvement processes involved in the overall clearance [AN Edginton, W. Schmitt, B. Voith, S. Willmann "A Mechamstic Approach to the Scope of Clearance in Children," Clin Pharmacokin. 2006; 45 (7) 683-704]. Furthermore, it should be considered that the effect and side effects of the drug can be influenced by age-related variations in the unbound fraction in the plasma. To account for this effect, the indication of plasma protein binding in adults and the indication of which plasma protein binds the drug mainly is required. For the most important plasma proteins such. B. serum albumin or α-glycoproteins are the age-related concentrations in the blood plasma known [Darrow DC, Cary MK. The serum albumin and globulm of newbom, premature and normal times infants. J Pediatr 1933; 3: 573-9., McNamara PJ, Alcorn J. Protein binding predictions in infants. AAPS PharmSci 2002; 4 (1): 1-8], so that differences in the free fraction can be calculated and taken into account. This mechanistic approach can be implemented in computer software.

Figure 2 shows the output window with that from the mechanistic model [A. Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children," Clin. Pharmacokin. 2006; 45 (7) 683-704] resulting age-dependent clearance curve in the child. Input parameters are the adult reference levels for the unbound fraction in plasma (plasma fu), biliary (plasma CLbil), hepatic (CLhep) and renal clearance (CLren), the relative contributions of enzymatic processes to hepatic clearance and the age of the child ( Age) and the indication of the main binding protein in plasma (albumin or glycoprotein).

The two approaches described can now be combined advantageously. A direct comparison of the two methods allows the definition of a (process-dependent) threshold for the age in which the intrinsic activity has reached the level of an adult. This is shown in this example for 15 different drugs from different indications. Table 1 summarizes the active substances, their degradation pathways and the reference values for the free fraction and the major binding protein in plasma in adults. These values are in the publication [AN Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children", Clin. Pharmacokin. 2006; 45 (7) 683-704):

Table 1

Figures 3 through 6 compare predictions with these two approaches for these agents with experimentally measured clearance values in children. Figure 3 shows the ratio of predicted to experimentally measured clearance in children as a function of age Age using the example of substances that are mainly eliminated via a single pathway (gentamicin, isepamicin, alfentanil, midazolam, caffeine, ropivacaine, morphine and lorazepam). The prediction in this case is based on the allometric scaling. Figure 2 clearly shows that the allometric approach, up to an average age of about one year, leads to a drastic overestimation of clearance in the child due to the failure to consider liver and kidney maturation. The clearance prediction for the same substances using the mechanistic model of Edginton et al. [Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children," Clin. Pharmacokin. (accepted for publication 2005)] is shown in Figure 4. In this case, the children under one year and the premature babies are in a similar litter interval as the children who are older than a year. A closer analysis of these data reveals the following thresholds for elimination processes for the age at which intrinsic activity reaches the adult level:

In Figures 5 and 6, the ratios of predicted and experimentally measured clearance values in children as a function of age are shown by way of example for substances which are eliminated via combinations of different degradation pathways (fentanyl, paracetamol, theophylline, ciprofloxacin, buprenorphine, lidocaine and levofloxacin). The prediction in Figure 5 is again based on the allometric approach, Figure 6 shows the prediction based on the mechanistic model of Edginton et al. [A. Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children," Clin. Pharmacokin. (accepted for publication 2005)]. Here, too, it becomes clear once again that the dispersion of the two models becomes comparable from an age of about one year; below one year, the clearance prediction on the basis of the mechanistic model is significantly better.

Both approaches can now be advantageously combined to form an overall model which, for premature babies, newborns and toddlers, up to the process-specific threshold value, performs a clariance scaling on the basis of a mechanistic modeling such as, for example, [Edginton, W. Schmitt, B. Voith, S. Willmann: "A Mechanistic Approach to the Scaling of Clearance in Children," Clin. Pharmacokin. (accepted for publication 2005)], and for children who older than this threshold, performs an allometric scaling based on the individual body weight. In the simplest case, a threshold of one year is assumed for all processes, which is the maximum value of all processes considered. For substances whose degradation processes are not known in detail, only the allometric method can be used, but this is then restricted for safety reasons for use in children older than one year.

B: Dosage in renal insufficiency: Example ciprofloxacin

This example shows how the diagnosis of "renal dysfunction" can affect the dosage of a renally excreted drug (exemplified by the antibiotic ciprofloxacin) using the following physico-chemical parameters of ciprofloxacin as input parameters for the simulation: Lipophilia (LogMA ) = 0.954, molecular weight = 331.3).

There are a number of clinical studies with ciprofloxacin in patients with varying degrees of renal dysfunction ["DRUS": Drusano et al., "Pharmacokinetics of Intravenously Administered Ciprofloxacin in Patients with Various Degrees of Renal Function" Antimicrob. Agents chemotherapy. 31 (6), 860-864 (1987); "WEBB": DB Webb et al "Pharmacokinetics of ciprofloxacin in healthy volunteers and patients with impaired kidney functions", J. Antimicrob. Chemotherap. 18Suppl.D, 83-87 (1986); "SHAH": A. Shah et al., Pharmacokinetics of Intravenous Ciprofloxacin in Normally and Renally Impaired Subjects V. Antimicrob Chemotherapy, 38, 103-116 (1996)]. A blood parameter is used as a measure of the degree of renal dysfunction , the so-called creatinine clearance (CL _cr ), which is often normalized to the body surface, typically classified into four groups according to the severity of the renal dysfunction:

For comparison with real patient data, PK-Sim ^{® was used to} create a virtual population consisting of 500 individuals. In terms of age, gender, size and weight distribution, this virtual population corresponded to the real comparison population, which is summarized in Table 2: Table 2:

Ciprofloxacin studies with patients with renal dysfunction. Data is given as mean (S.D.) or range [minimum - maximum]. iv: intravenous administration; n.r .: not reported.

ON

In healthy adults, total ciprofloxacin plasma clearance is approximately 7.6 ml / min / kg. Approximately 2/3 of intravenous ciprofloxacin is excreted unchanged in the urine (equivalent to a renal clearance of 5.0 ml / min / kg). To generate individuals with renal dysfunction of varying severity in the virtual population, renal clearance was linearly interpolated as a function of creatimine clearance from 5.0 ml / min / kg in group A patients (no renal dysfunction) to 0.0 ml / min / kg in individuals without any renal function (Figure 13a). Renal dysfunction is about (z. B. of serum albumin "HS A") also typically proteins with a reduction in Plasmapro- connected, which in turn affects the unbound fraction of a substance in the plasma. The unbound fraction (f _u) is related to the Volume fraction of serum albumin (^ _SA ) we find together:

fu = l / [0 - fHSA) + fHSA K _HSA ]

K _HSA herein represents the albumin / plasma partition coefficient. In healthy individuals, KHSA can be calculated by transforming the equation from the values for f _u (70%) and f _H s _A (2.2%, equivalent to 4.0 g / dL) to K _HSA = 20.5. In individuals with reduced creatinine clearance (<15 ml / mm), serum albumin is reduced by approximately 30% (f _H s _A = 1.5%, corresponding to 2.8 g / dL) [Viswanathan et al., "Serum levels in different stages of

type 2 diabetic nephropathy patients ", Indian J Nephrol, 14, 89-92 (2004)] .The free plasma fraction can be expressed as a function of creatimine clearance by a linear interpolation (see Figure 13b).

Each virtual individual was now stochastically assigned a creatinine clearance, from which a renal clearance (Fig. 7) and a free fraction (Fig. 8) could be determined as input parameters for PK-Sim ^® using the curves shown in Figures 7 and 8 , After simulating ciprofloxacin pharmacokinetics in the virtual population, the results can be compared with those of the real population (Figure 9). The left-hand column of Figure 9 shows the simulated area-of-area concentration concentration (AUC), the volume of distribution, and the maximum dose-normalized concentration after a one-hour infusion for the virtual individuals. In the right column, the corresponding results of the experimental studies are shown (symbols, mean and SD) together with the mean (thick line) and the 5% and 95% percentiles (dashed lines) from the simulation, respectively. The comparison of the simulated and experimentally determined pharmacokinetic parameters of ciprofloxacin in patients with renal dysfunction shows that the simulation is qualitatively and quantitatively capable of correctly describing the influence of decreased renal excretion on pharmacokinetics. From this description, a dose adjustment can immediately be deduced from a substance-specific target variable (eg C _n ^ _x or AUC). C: Dosage in a severely overweight patient: Example Ciprofloxacin

Caldwell and Nilsen published a case study on the administration of ciprofloxacin in a severely obese patient [Caldwell JB and Nielsen AK, Intravenous Ciprofloxacin Dosing in a Morbidly Obese Patient, Annais of Pharmacotherapy 28 (1994)]. The male patient was 57 years old and weighed 226 kg at the time of treatment. His kidney and liver function were normal. Therapeutic dosing was calculated on the basis of further published cases of ciprofloxacin administrations in overweight patients by LeBeI et al. [LeBeI M, Kinzig M, Allard S, Mahr G, Boivin G, Sörgel F. Ciprofloxacin disposition in obesity (abstract 601). Presentation at the 31st Interscience Conference on Antimicrobial Agents and Chemotherapy, Chicago, September 29 - October 2, 1991]. However, the overweight patient population described herein weighed only 111 ± 20 kg, that is, on average less than half of Caldwell and Nilsen's severely obese patients. As a result of the empirical estimation, the patient was given a single dose of 800 mg ciprofloxacin as an intravenous infusion administered over 60 minutes twice daily at 12-hour intervals for several days. To check the estimated dose, a blood sample was taken from the patient on the fourth day of treatment about 20 minutes after the end of an infusion, and the plasma level of ciprofloxacin was determined experimentally. The measured value was 4.2 mg / L [Caldwell JB and Nielsen AK, Intravenous Ciprofloxacin dosing in a morbidly obese patient, Annais of Pharmacotherapy 28 (1994)]. This selective measurement was in the therapeutically effective range and below the plasma concentration of 10 mg / L, which is critically assessed as toxicologically critical.

To simulate ciprofloxacin administration in this severely overweight patient, the same physicochemical parameters of ciprofloxacin as in Example B were used again. The weight of the patient was set at 226 kg, the height (due to missing information from the publication) as normal, d. H. 176 cm, assumed.

Based on reported normal renal and hepatic function, the mean plasma clearance of ciprofloxacin in adults was 7.6 ml / min / kg. The simulation of the described dosing regimen (see Figure 10) in the severely overweight patient resulted in a plasma concentration of 4.1 mg / L for the sampling time, which is almost exactly the same as the experimentally determined value (4.2 mg / L) , This match is further evidence of the validity of the simulation model. In addition, the simulation shows that the timing of sampling (20 minutes after stopping an infusion) is not (as intended) the maximum ciprofloxacin concentration during therapy, due to the rapid distribution kinetics of ciprofloxacin. The simulated maximum The concentration in the plasma at the end of an infusion was approximately 9.2 mg / L at equilibrium, which was significantly higher than the value measured at the time of sampling and very close to the toxicologically relevant limit of 10 mg / L. For safety reasons, an infusion over a longer period, eg. Over two hours (this dosing scheme is shown comparatively in Figure 11). This example demonstrates the superiority of the patient-individual calculation of the dosage and the dosage scheme based on physiology-based pharmacokinetic models compared to the conventional, empirical approach, which draws on as similar as possible literature described comparison cases. In this specific case, the similarity of the patient to be treated (body weight 226 kg) with the published patient collective (mean body weight 111 kg) was only weakly pronounced. Although the estimate of the total dose of ciprofloxacin administered resulted in a reasonable result (800 mg as a single dose twice daily), the chosen regimen resulted in maximum concentrations that were very close to the toxicologically relevant threshold. This could have been prevented by the use of the device according to the invention.

D: Dosage in co-medication: paclitaxel and cyclosporin

The risk of drug-drug interactions is particularly high in seriously ill and multimorbid patients. There are numerous interaction studies, for example, with known inhibitors of p-glycoprotein transporter system (Pgp), which z. B. occurs in the intestine and there may have an influence on the intake of orally administered drugs, or in the liver an influence on the excretion. Important known Pgp inhibitors are ketoconazole, verapamil or cyclosporin. In the following, the interaction of paclitaxel with cyclosporin is shown as an example that the pharmacokinetic effect can be described quantitatively with high accuracy by means of the physiology-based simulation.

Paclitaxel is a cancer drug that is a substrate for Pgp. Upon oral administration of paclitaxel, Pgp-mediated active efflux results in a relatively low bioavailability of about 3%. Prompt administration of the Pgp inhibitor, such as cyclosporin, inhibits active efflux and results in an approximately 7-fold increase in systemic exposure of paclitaxel (bioavailability approximately 22%). This clinical finding is quantitatively comprehensible with the physiology-based pharmacokinetic simulation model PK-Sim ^® .

Table 3 shows pharmacokinetic parameters such as systemic exposure (expressed as area under the plasma concentration-time curve, AUC), maximum plasma concentration (Cmax), as well as the times from which the plasma concentration is above 0.1 μM or 0.5 μM. The calculated values agree very well with the experimentally measured values.

Table 3

* rounded value

It is thus possible to simulate the interaction of two drugs administered in a timely manner. The simulation can then be used to derive dosage instructions. In the present case, for example, the simulation recommendation would be to reduce the paclitaxel dose by 90% when coadministered with cyclosporin.

Claims

claims

1. Vorπchtung for providing a drug dose comprising an input unit

(1) for input of individual patient information (101), a calculation unit

(2) for the calculation of the dose of the drug and, where appropriate, the optimal dosage regimen, and an associated automatic dosing device

Medicines (3),

characterized in that the calculation of the medicament dose in the calculation unit (2) by a rational mathematical simulation model (205) using physiological information (201), pathological information (202), medicament-specific information (203) present in the calculation unit (2) ) and, where appropriate, information on additionally administered medicaments (204).

2. Device according to claim 1, wherein the input unit (1) is a handheld device for manually inputting the individual patient information (101) or a chip card reader for reading the individual patient information (101).

3. Device according to claim 1 or 2, wherein the rational mathematical model (205) is selected from allometric Skaherfunktionen or physiology-based Pharmako- Ketetikmodellen.

4. The device of claim 3, wherein the rational mathematical model (205) is a dynamically generated physiologically based pharmacokinetic / pharmacodynamic

Simulation model is.

5. Device according to one of claims 1 to 4, wherein the individual patient information (101) are selected from age, sex, race, body weight, body size, body mass index, lean body mass fat free body mass, gene expression data, diseases, allergies, medication , Kidney function and liver function.

6. Device according to one of claims 1 to 5, wherein the physiological information (201) are selected from age, sex, race, body weight, height, body mass index, lean body mass fat free body mass, gene expression data, diseases, allergies, medication , Kidney function and liver function.

The device according to any one of claims 1 to 6, wherein said pathological information (202) is selected from age, sex, race, body weight, height, body Mass index, lean body mass, fat free body mass, gene expression data, diseases, allergies, medication, kidney function and liver function.

8. Device according to one of claims 1 to 7, wherein the drug-specific information (203) are selected from lipophilicity, free plasma fraction, blood plasma ratio, distribution volume, clearance, type of clearance, clearance proportions, type of

Excretion, dosage regimen, transporter substrate, PD endpoint and side effects.

9. Device according to one of claims 1 to 8, wherein the information on additionally administered drugs (204) are selected from lipophilicity, free plasma fraction, blood plasma ratio, volume of distribution, clearance, type of clearance, clearance proportions, type of excretion , Dosing regimen, transporter substrate, PD endpoint and side effects.

10. Device according to one of claims 1 to 9, wherein the transmission of information from the input unit (1) to the calculation unit (2) and / or from the calculation unit (2) to the device for metering medication (3) is wireless.

11. A method for calculating a drug dose based on individual patient information (101), characterized in that the calculation of the drug dose in a calculation unit by a rational mathematical simulation model (205) using present in the calculation unit physiological information (201), pathological information (202), drug-specific information (203) and optionally information on additionally administered drugs (204).