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RESEARCH ARTICLE Pharmaceutical Nanotechnology

The Role of Payload Hydrophobicity in NanotherapeuticPharmacokinetics

POVILAS NORVAISAS, ARTURAS ZIEMYS

Houston Methodist Research Institute, Department of Nanomedicine, Houston, Texas 77030

Received 31 March 2014; revised 26 March 2014; accepted 7 April 2014

Published online 6 May 2014 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/jps.23996

ABSTRACT: Although drug delivery with nanovectors is regarded as one of the paradigm-shifting advances in modern medicine, thecompatibility and performance of drugvector formulations have not been systematically studied in terms of their physicochemistry andpharmacokinetics (PKs). The drug delivery systems (DDSs), currently available in clinics or trials, were analyzed based on hydrophobicityand anatomical therapeutic chemical (ATC) classification of drug payloads. Four major types of DDSs differentiated based on DDS structureand drug hydrophobicity, where payload hydrophobicity decreased: micelles, serum albumin, liposome membrane, and liposome interior.A strong relationship between the increase in half-life in DDS formulation and drug hydrophobicity was found with up to 200-fold greaterincrease for hydrophilic drugs. The analysis results seemingly integrated PKs, ATC, and hydrophobicity to reinforce the development oroptimization of drug delivery vectors and their formulations. C2014 Wiley Periodicals, Inc. and the American Pharmacists Association J

Pharm Sci 103:21472156, 2014Keywords: log P; liposomes; albumin; micelles; nanoparticles; formulation vehicles; formulation; lipoproteins; pharmacokinetics; physic-ochemical

INTRODUCTION

Advances in nanotechnology and material sciences havespawned many approaches to enhance drug delivery.13 Themajority of these methods rely on nanoparticles and mi-croparticles, which span a wide range of sizes and shapes.These particles serve as delivery vectors46 for the trans-port of image-contrast agents7,8 and drugs. A large varietyof particles are available as vectors, such as lipid particles, 9

liposomes,10 micelles,11 serum albumin particles,12 fullerenes,13

carbon nanotubes,14 dendrimers,15 silica, metallic particles(gold, iron oxide, etc.),16 polymeric particles [e.g., poly(lactic-co-glycolic acid)],17 and hydrogels.18 Drugs that employ vector-based drug delivery systems (DDSs), such as Doxil

TM, have been

extensively used in clinical medicine.19 Over the last decade,more DDSs have entered clinical trials, and even more havebeen actively studied,20 especially in the treatment of cancers.

Some drug delivery strategies involve the modulation anddifferentiation of vector biodistribution in organs or tissues.Therefore, such approaches increase the delivery of the ther-apeutic payload in certain organs.21 DDSs have gained ben-efits over classical drugs in pharmacokinetics (PKs), such asincreasing circulation time or reducing toxicity. However, the

magnitude of benefits is sometimes poorly defined, as is refer-enced in several papers.22,23 Vectors have their own PK profilesthat may differ from those of therapeutic substances because

Abbreviations used: ATC, anatomical therapeutic chemical; DDS, drugdelivery system; MPS, mononuclear phagocyte system; HSA, human serumalbumin; BSA, bovine serum albumin; MTD, maximum tolerated dose; AUC,area under the curve.

Correspondence to: Arturas. Ziemys (Telephone: +713-441-7320; Fax:+713-441-7438; E-mail: [email protected])

Povilas Norvaisas present address is Department of Biothermodynamics andDrug Design, Vilnius University, Vilnius LT-02241, Lithuania.

Thisarticlecontains supplementary materialavailable fromthe authors uponrequest or via the Internet at http://onlinelibrary.wiley.com/.

Journal of Pharmaceutical Sciences, Vol. 103, 21472156 (2014)C 2014 Wiley Periodicals, Inc. and the American Pharmacists Association

of their size and surface properties. As a result, the vectors cir-culation and interactions with cells may be impacted, thus re-sulting in altered biodistribution. Carriers can also employ theenhanced permeability and retention (EPR) effect.24 The EPReffect is a result of two properties of tumor tissue: leaky vas-culature with fenestrations of up to several microns in size,25

and greater than normal retention within the tumors intersti-tial fluid. Because of EPR, most polymeric drugs accumulatein tumor tissues at concentrations that are five to 10 times

or 10 times higher than those in plasma or normal tissues,respectively.24 PEGylated (also referred to as sterically sta-bilized or Stealth

TM) liposomes display inhibited interactions

with plasma proteins and mononuclear phagocytes, resultingin prolonged circulation times and increased accumulation inthe interstitial fluid of tumors at levels comparable to thoseof reticuloendothelial system-rich organs.26 The mononuclearphagocyte system (MPS), which consists of phagocytic cells inthe lymph nodes, spleen, or Kupffer cells in the liver, sequestercirculating particles, including carriers.27 This effect is not de-sired frequently, and delivery agents can be chemically alteredto make them invisible to MPS. An example of this alterationis a modification with PEG.28 Because of the EPR effect andMPS, the PKs of vectors are very different from those of smallmolecule drugs. A drug that is associated with a carrier willadopt the carriers PK profile,29 both with advantages and neg-ative consequences.

The efficacy of DDSs depends on many properties, includinghow much of a drug can be loaded into a carrier. If no covalentbinding is involved to retain drugs inside the carrier matrix, theloading and release of drugs from such DDSs rely on diffusionand concentration gradients, as well as the degradation of thecarrier. Because many different materials of varying composi-tions are possible for the production of vectors, each DDS willhave a specific affinity for a particular drug. Drug hydropho-bicity may influence its loading and release, making it more orless efficient.3038 Moreover, the hydrophobicity of a carrier may

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2148 RESEARCH ARTICLE Pharmaceutical Nanotechnology

also have the same effect.3941 However, the carriers influenceis not significant because of the tendency for different immisci-ble systems to have comparable partitioning42 (Fig. A.1).

LogP is the logarithm of a partitioning coefficient betweenthe waterand octanol phases for a chemical compound. The par-titioning of drugs is a complex process that depends on manyphysicochemical aspects, where logP provides an integratedphenomenological measure. Log D is an alternative parame-ter that accounts for the charge of drug at different pH levels.However, scarce experimental data are available for logD. Inaddition, it is less reliable than log P because of cumulativeerror in log P and pKa measurements.

43 Log P is one of thefive parameters in the Lipinski rules for drug likeliness, and isemployed in drug discovery.44 Because logPcan be easily eval-uated with good accuracy using computational algorithms,45 itis widely accepted and can be found in many databases.

In addition to the drugs interactions with the drug mediaand vector matrix, drug loading and release may also be af-fected by the surrounding physiological media, which can beexpressed in terms of the logP value of the drug. Studies in-

vestigating the systemic basis of how drugs and vectors may

be coupled are limited.46

Because logP may affect both drugdelivery and PK, DDSs can be created in response to specifictherapeutic aims to maximize the synergism of the drugs and

vectors physiochemical properties.We assume that there should be certain level of compati-

bilities between drug vectors and drugs. Therefore, we haveanalyzed DDSs by investigating the physicochemical, PK, andtherapeutic properties of drugs to establish relations leading tobetter systemic knowledge about existing and future DDS.

METHODS

Drug Information

DrugBank is a freely available database, which contained 6711total entries at the time of the study.47,48 The entire dataset, in-cluding small molecules and biomolecules, was parsed in XMLformat and analyzed with Knime software.49,50 Drug proper-ties, including log P, bioavailability, and half-life (t1/2), wereextracted with the accompanying structures. The parsing andanalysis of the dataset were automated to prepare informationfor further statistical evaluation. Most of the logP values forsalt-free drugs were taken from an extensive study by Han-sch and Leo.51 In this study, we will refer to experimentallyestablished logP values in the octanolwater system, unlessotherwise stated. Missing logP values in Table 1 were supple-mented by calculations with XlogP implementation in Knime

software,49

and they are marked with asterisks. The perfor-mance of XlogP was validated with DrugBank drugs, and theresults are displayed in Figure A.2. Drug bioavailability andt1/2 were extracted with regular expression text analysis tools,and then verified by visual inspection.

After DrugBank was updated on August 2, 2013, the exper-imental logP values of several drugs (doxorubicin, daunoru-bicin, floxuridine, and fluorouracil) were altered, although theofficial database snapshot was not updated at the time ofmanuscript submission. The changes for both floxuridine andfluorouracil were very minor (0.2 and 0.1 logPunits). However,the logP values for doxorubicin and daunorubicin drasticallyincreased from0.5 and 0.1 to 1.27 and 1.83, respectively. Thischange seemed ambiguous because of the known solubility of

these compounds. Therefore, we averaged the logPvalues thatwere documented in various studies. The calculated averagelogPvalue for doxorubicin and daunorubicin were0.26,44,5258

and 0.72,5759 respectively. All of these values were determinedat pH 77.2 for salt-free drugs.

Data regarding the anatomical therapeutic chemical (ATC)classification was collected, along with defined daily doses andadmission routes.60,61 Most of the drugs in DrugBank have

ATC codes assigned to them, and some have several codes be-cause of the use of the same chemical compound for differentindications. The ATC code consists of several levels that de-scribe the anatomical, therapeutic, and pharmacological clas-sification of a specific chemical moiety (http://www.whocc.no/atc/structure and principles/). An extensive ATC code descrip-tion is given in Tables A.1A.2. In this study, drugs in Drug-Bank were grouped according to the first, ATC (1), and second,

ATC (2), levels of ATC classification: anatomical location andtherapeutic action.

Statistical Analysis

The extracted data were analyzed using the StatSoft Statis-

tica 10 software. We chose to directly analyze logP values in-stead of the partitioning coefficient (P), because logP exhibitsnormal distribution, which was also observed for all differentsubgroups of drugs. P distribution is highly asymmetric andcannot be easily parameterized. Datasets that were categorizedinto ATC (1) and ATC (2) classifications and exhibited normaldistribution was evaluated using parametric one-way analysisof variance. After the heterogeneous categories were identi-fied and homoscedasticity was proven by the Levene test, pair-wise comparisons of categories were performed with Tukeyshonest significant difference test for samples with unequal N(Spjotvoll/Stoline). This approach allowed us to identify sig-nificantly different drug groups at both ATC (1) and ATC (2)

levels (Tables A.3. and A.4.). For box-and-whisker plots withboth normal and nonparametric distributions, only categorieswith more than four members were used.

Pharmacokinetics

Detailed PK analyses were performed for DDSs with sufficientdata. We used the mean values of PK parameters at the samedosage levels, and preferentially selected the maximum toler-ated dose (MTD) or the most commonly used dose of the freedrug if the MTD was unknown. In the case of paclitaxel, therewas no carrier-free formulation of the drug; therefore, we usedTaxol

TMas a reference, regardless of its use of CremaphorEL.

RESULTS

This study depended on well-defined reference data. Whatmakes a good DDS depends on the purpose of that particularsystem. To provide the least-biased analysis, we assumed thatDDSs that are currently employed in clinics or clinical trialsare sufficiently well developed. Their usage in clinics suggeststhat the given DDS meets some clinical and engineering expec-tations. Therefore, the study began with a brief analysis of suchDDSs, where the physicochemical boundaries were evaluatedand compared against available drugs and therapeutic classi-fiers. The second part of the study dissected the PK propertiesof the DDSs, and established correlates to couple the propertiesof drugs and DDSs with their PK. Less relevant data related to

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RESEARCH ARTICLE Pharmaceutical Nanotechnology 2149

Table 1. Classification of nanotherapeutics in clinical trials and clinics based on logP values and carrier types. The active ssubstance, brand

name, average diameter of the carrier (), logP, indications, and information sources are given for each drug. Average log P values were

calculated for each carrier type, and only unique drug substances were considered

Type Drug Brand Name (nm) Log PD Average LogPD Indications

Liposome interior Amikacin MiKasomeTM

50 7.4 1.1 Infections

Cytarabine CPX-351 100 2.8 Cancer

Cisplatin SPI-077 110 2.2 Cancer

Floxuridine CPX-1 110 1.2 CancerFluorouracil 5-FU 130170 0.9 Cancer

Doxorubicin MyocetTM

150180 0.3 Cancer

Doxorubicin Doxil/Caelyx 100 0.3 Cancer

Doxorubicin SarcodoxomeTM

0.3 Cancer

Doxorubicin MCC465 143 0.3 Cancer

Lurtotecan* OSI/NX-211


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Percentag

e

P

ercentage

P

Figure 1. The distribution of experimental logPvalues for DrugBank

drugs (columns). The Gaussian fit (red line) over all drugs is centered

at 1.9. There is a significant overlap between the logP coverage of the

DDSs (blue line) and the population of the drugs.

Hydrophilic and hydrophobic vectors may comprise 29% and57% of all investigated drugs, respectively. As shown in Table1 and Figure 1, the vectors encompass the logPrange between7.4 and 4.3. The remaining 14% seem to be incompatible withcurrently available vectors. The major carriers are liposomes,which encompass 70% of all drugs, with 28% sequestered inthe liposome aqueous core and 42% in the liposome membrane.

Albumin appears to be the second major carrier type with logPvalues ranging from 1.8 to 4.3, thus representing 42% of allDrugBank drugs. Finally, micelles appear to have a slightlynarrower range of compatible logPvalues between 2.4 and 4.2.However, this group coincides with the peak of the histogramin Figure 1 and encompasses 29% of potential drugs. The log

P range between 2.4 and 4.2 not only contains most of the

drugs, but is shared among micelles, liposome membranes, andalbumin particles. Therefore, these drugs possess the highestpotential of success with vectors, as one can select from a large

variety of PK and biodistribution profiles that are associatedwith different DDS types.

The majority of drugs demonstrate low water solubility intheir salt-free forms, because of comparably high log P val-ues. This coincides with the active components of carriers,such as the internal milieu of liposome membranes and mi-celles. The partitioning of drugs and hydrocarbons between wa-ter and different lipophilic phases are similar for compoundswith low logP values (up to logP = 3), whereas larger par-titioning differences appear in compounds with larger log P

values.62,63

The partitioning ofn-alkanes in water-octanol andwater-hexadecane is only slightly higher in the lipophilic phaseof hexadecane42 (Fig. A.1). This suggests that the carrier ma-trix should be of a similar or larger log P value to provide aphase that would offer efficient hydrophobic drug loading andcompatibility. Results from our analysis reveal that drugs withspecific logP values are largely being used with a particularcarrier type.

Differences in log P among drugs with various therapeu-tic aims can lead to alterations in the loading and release ofthose drugs from the carriers. The release rate seems to beclosely related to the partitioning of the drug. For example,more hydrophobic drugs are released at slower rates. The re-lease of Ftorafur is slower than that of 5-FU, which is less

lipophilic.30 Lipophilic compounds, including rapamycin and cy-closporine A, partition predominantly into micelle cores and arereleased very slowly.39 For antiepilepticdrugs, rufinamide is themost hydrophobic with the slowest release.36 The release of dif-ferent corticosteroids decreases with increasing lipophilicity.37

The properties of the matrix of the carrier itself also modulatethe release rate depending on its hydrophobicity. However, ac-cording to the data for linear alkanes whose partitioning wasinvestigated in different immiscible systems, such modulationshould be minor42 (Fig. A.1). Therefore, the drug release pro-cess depends heavily on the partitioning of the drug (Fig. A.4.),which can be more important than the reduction of drug diffu-sion by the carrier matrix.64 Further discussion on this topic isprovided in the Appendix section Diffusion Mass Release.

Anatomical Therapeutic Chemical

The analysis was extended to determine whether the thera-peutic and anatomical groups of drugs have divergent log P

values and exhibit hydrophobicity associated correlations be-tween ATC and carriers. Experimental logP values of Drug-

Bank entries were grouped according to their ATC (1) (Fig. A.4;Table A.8) and ATC (2) codes, which represent the anatomicallocations of drug action and therapeutic grouping correspond-ingly.

By following the ATC (2) classification, 88 therapeutic druggroups were obtained. Seventy one out of the 88 groups hadat least five members and were used for further analysis.Fifty-eight groups were significantly divergent with the meansspread in seven logP units (Tables A.4 and A.9). The distribu-tion of all 71 groups is shown in Figure 2. The variance of log

P within a single set was very different at times. In some ofthe groups, high variances in logP (e.g., C05 and B01) coveredalmost the entire range of values found in DrugBank, whereasother groups had very tight distribution (L03 withN= 12, D07

with N= 39, N01 with N= 33, and A14 with N= 5). Thecorrelation of logP values with ATC suggests that particulardrug groups may be used with a specific carrier type (Table

A.9). Therefore, the degree of hydrophobicity may be used todiscriminate drugs by anatomical locations of their actions andtherapeutic grouping, as well as to identify the most optimalcarrier (Table 1).

Pharmacokinetics

We further extended our study of DDSs by analyzing their PKsandcomparing them to those of free drugs. The potentialof drug

vectors to prolong the half-lives of drugs is shown in Figure 3.

Our analysis revealed that many drugs have relatively shorthalf-lives: 68% of DrugBank drugs have a t1/2 shorter than12 h, 50% shorter than 6 h, and 10% less than 1 h (Table A.10.).Figure 3 shows a comparison oft1/2changes for compounds thatare currently used in trials and clinics, where the change int1/2is depicted by arrows and sorted by drug hydrophobicity. Mostof these drugs have short half-lives (


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Figure 2. The logPdistribution in different therapeutic groups of DrugBank drugs based on ATC(2) levels. The group all represents all drugs

in the DrugBank. Compatibility ranges for different delivery vehicles are color-coded in the background with the number of drugs for each group

shown by columns at the top.

( )

Figure 3. The histogram shows the distribution oft1/2 of DrugBank

drugs with the percentage of the whole population displayed on top of

the bars. The arrows illustrate the change in the t1/2 of a particular

drug substance following DDS delivery, starting at the t 1/2 of a pure

drug and ending at the t 1/2 of vector formulation. The text on top of

the arrows indicates the brand names (active substance, log P) and

multiplicator oft1/2 change.

DDSs depends on many important aspects, each carrier groupis briefly reviewed below, including representative cases.

Liposome-Based Formulations

AmBisome carries the anti-infective drug, amphotericin B, ina liposome membrane. The mean t1/2 of total amphotericin Bafter liposomal administration is 152 h, compared with 362h of the free drug in plasma.65 However, with a threefold in-crease in the dose, the Cmaxis eightfold higher and the medianarea under the curve (AUC) is ninefold higher under conditionswhere the total available drug pool is analyzed.66 Doxorubicin,

a popular anticancer drug, is already used with carriers in twoformulations (Doxil/Caelyx, Myocet) with one undergoing clin-ical trials (MMC-465).t1/2 was prolonged in all three formula-tions; however, the PEGylation of Doxil causes toxicity in theform of hand-foot syndrome, as well as other dose-limiting sideeffects.67 All three DDSs of doxorubicin exhibit different ad-

vantages over the free formulation of the drug. CPX-351 is abilameral liposomal formulation, in which cytarabine and dox-orubicin are maintained in the aqueous liposome interior at aratio of 5:1. This carrier prolongs the terminal t1/2 of cytara-bine from 12 min to an average of 34 h.68,69 The terminal t1/2of daunorubicin increases from 17 to 25 h on average.68,70,71

Equivalent therapeutic efficacy is achieved with doses lowerthan conventional formulations.72 CPX-351 employs a synergyof the drugs.68

Albumin-Based Formulations

Albumin-bound paclitaxel (nab-paclitaxel; Abraxane) uses analbumin nanoparticle as its carrier.73,74 Human serum albumin(HSA) nanoparticles possess high drug loading efficiency with-out the risk of toxicity. However, circulation of the drug cannotbe substantially prolonged, as the mean t1/2for Abraxane is sim-ilar to that of the solution-based paclitaxel (Taxol). Even though

a higher initial concentration of the drug can be reached, sys-tematic exposure remains the same as with Taxol, because ofits increased rate of clearance.75,76 The main advantages of the

Abraxane formulation include reductions in toxicity, a 3.8-foldincrease in peak plasma concentration, and a 10-fold increasein the maximum concentration of the unbound drug.75,77

Micelle-Based Formulations

Genexol-PM is a PEGpoly-(D,L-lactide) polymeric micellar for-mulation of paclitaxel. It was developed to overcome toxicitiescaused by CremaphorEL (Taxol). Genexol-PM has a lower peakplasma concentration and lower systematic exposure (AUC)with a slight increase in t1/2 on average. Similar to Abraxane,

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Figure 4. Augmentation of drugt1/2 in vectors, according to drug hy-

drophobicity. Amikacin in MiKasome was excluded from the regression

as an outlier (marked by an asterisk).

it has a good toxicity profile with no known side effects. Un-like Taxol, Genexol-PM does not require premedication withsteroids or antihistamines.76 A threefold increase in MTD isobserved, and the drug levels are two to three times higher in

various tissues, including tumors.78

Competitive Drug Binding in Blood Plasma

While summarizing the changes in t1/2 in all analyzed DDSs,we noticed that the hydrophilic and hydrophobic drugs havecompletely different PKs when used with vectors. As shown inFigure 4, the t1/2 of hydrophilic drugs is prolonged to a greaterextent than that of hydrophobic drugs, regardless of a carriertype. The t1/2 of hydrophobic drugs is barely prolonged by the

vector. This may be explained by the observation that hydropho-

bic drugs can quickly associate with blood serum proteins.7981

Findings from earlier studies suggest that HSA, alpha-1-acidglycoprotein, and low- and high-density lipoproteins (LDL andHDL) may be responsible for the lack of change in the t1/2 ofhydrophobic drugs.82

Human serum albumin is the most common protein in theblood with a circulation time of up to 19 days.73 It binds hy-drophobic drugs in several pockets on its surface,73,83,84 andmay also trigger the dissociation of certain micelle systems.85

Using results on drug binding to HSA,86 our analysis confirmedthat logPvalues and drug binding have a strong positive corre-lation (Fig. A.5). The presence of bovine serum albumin (BSA)(almost identical to HSA) in solution (4% [w/w]) doubles the

release of rapamycin from PEGPCL micelles.39

The associa-tion of BSA with the free drug allows more of the hydrophobicdrug to be released, compared with that observed by parti-tioning alone.39 Drugs with a high affinity to serum albumin(>95% bound) require correspondingly higher doses to achieveeffective concentrations in vivo.79 The amount of free hydropho-bic drug can be significantly reduced67; in the case of ampho-tericin B, more than 90% of the drug that is injected with lipo-somes is recovered as HSA bound.65 LDL and HDL may alsoaccount for the different PKs of hydrophobic drugs. The asso-ciation or interaction with cholesterol, triglycerides, phospho-lipids, and LDL/HDL seem to be general property of vectors. 87

For liposomes incubated with human plasma, up to 67% ofhydrophobic drugs are recovered in the HDL fraction.82 The

t

(

(

(

(

(

(

Figure 5. The changes in PK parameters of drugs after they are used

in DDSs. The change in AUC was kept in a linear scale for clarity. The

red lines separate DDSs into groups, based on changes in their t1/2and

Cmax.

above-mentioned blood components eventually serve as naturaldelivery systems (NDSs) that sequester drugs from synthetic

vectors, and can be employed for drug delivery.82,88

Hydrophobicity and PKs

We analyzed hydrophobicity of the DDSs and changes in theoverall PK profiles of drugs in formulations with and withoutcarrier. For 10 formulations with sufficient data, we comparedthe average values of AUC, Cmax, and t1/2(Table A.11). All DDSsshowed an increase in at least one of the parameters of PK,as seen in Figure 5. On the basis of the changes in t1/2 andCmax, three DDS groups were identified. The first group had anincreased t1/2 and decreased Cmax, like Genexol-PM CPX-351(daunorubicin). The second group had a decreased t1/2 and in-creasedCmax, like AmBisome and DaunoXome. The third grouphad an increased t1/2 and Cmax, like Doxil, Myocet, MCC465,CPX-351 (cytarabine), and SPI-077. All DDSs with drugs for-mulated in the liposome interior showed a significant increase

in the AUC, except for Abraxane, which had its AUC reducedcompared with Taxol.The hydrophilic drugs had a greater increase in the AUC

compared with the hydrophobic drugs. Even though the AUCis considered to be a measure of exposure to the drug, it can-not be generalized whether the increase in this parameter isfavorable. All of the 10 DDS formulations have benefited fromreductions in systemic toxicities, regardless of changes in PK.Both Genexol-PM andAbraxane were created to avoid toxicitiesassociated with CremaphorEL in Taxol. Small changes in PKparameters are likely to be caused not only by hydrophobicity,but also by the fact that we are comparing parameters againstthose of Taxol, which already has solubilizing agent. AmBi-some has benefited because amphotericin B is more effectively



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Table 2. The models of relation between PK and logP. The asterix

denotes a base 10 logarithm of a ratio in a parameter

LogP and PK Relations R2 p

AUC 0.52Cmax + 0.38t1/2 0.31 logP + 0.41 0.87


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