e u r o p e a n j o u r n a l o f p h a r m a c e u t i c a l s c i e n c e s 2 7 ( 2 0 0 6 ) 91–99
`
`a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m
`
`j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e j p s
`
`A strategy for preclinical formulation development using
`GastroPlusTM as pharmacokinetic simulation tool and a
`statistical screening design applied to a dog study
`
`Martin Kuentz ∗, Sonja Nick, Neil Parrott, Dieter R ¨othlisberger
`
`F. Hoffmann-La Roche Ltd., Pharmaceutical and Analytical R&D, Bldg./Lab. 072/338, Grenzacherstr., CH-4070 Basel, Switzerland
`
`a r t i c l e
`
`i n f o
`
`a b s t r a c t
`
`Article history:
`Received 21 February 2005
`Received in revised form 11 August
`2005
`Accepted 20 August 2005
`Available online 10 October 2005
`
`Keywords:
`Simulation
`Bioavailability
`Dog studies
`Factorial design
`Clinical formulation
`
`The aim of this paper is to propose a pharmaceutical risk assessment strategy that goes
`beyond the usual characterisation of a clinical candidate molecule according to the bio-
`pharmaceutical classification system (BCS). This strategy was evaluated for a new CNS drug
`with poor solubility and good permeability. In a first step, GastroPlusTM was used to simulate
`the absorption process based on preformulation data. These input data involved a physico-
`chemical drug characterisation including drug solubility measurements in simulated phys-
`iological media, as well as permeability determination. Further computer simulations were
`conducted to determine the sensitivity to changes of selected input values. Thus, oral
`bioavailability prediction was studied as a function of the particle size and drug solubility.
`The second part of the presented strategy for preclinical formulation development was to
`test specially designed formulations in a 23 screening factorial plan using the dog as the
`animal model. The factors were the dosage form, food effect and dose strength. One of the
`two experimental formulations was a capsule filled with the micronised drug, whereas the
`other formulation was a surfactant solution of the drug. Accordingly, a “worst case” formu-
`lation was compared with a “best case” drug solution over the clinically relevant dose range
`in fasted and fed dogs. The results of the computer simulation indicated that a fraction of
`the dose is dissolved in the stomach and precipitates partially in the small intestine. The
`simulation predicted almost full drug absorption during the GI transit time. Interestingly,
`the simulation implies that stomach drug solubility had little impact on overall fraction
`absorbed. The results also showed that changes of particle size and reference solubility
`within two orders of magnitude hardly affected the oral bioavailability. This in silico deduc-
`tion was subsequently compared with the results of the dog studies. Indeed a surfactant drug
`solution showed no clear biopharmaceutical superiority over a solid capsule formulation on
`the average of both dose strengths in fasted and fed dogs. Despite the substantial variability
`of the in vivo data, the factorial screening design indicated marginal significant interaction
`between the dose level and feeding status. This can be viewed as a flag for the planning of
`further studies, since a potential effect of one factor may depend on the level of the other.
`In summary, the GastroPlusTM simulation together with the statistically designed dog study
`provided a thorough biopharmaceutical assessment of the new CNS drug. Based on these
`findings, it was decided to develop a standard granulate in capsules for phase I studies.
`More sophisticated formulation options were abandoned and so the clinical formulation
`development was conducted in a cost-efficient way.
`
`© 2005 Elsevier B.V. All rights reserved.
`
`∗ Corresponding author. Tel.: +41 61 688 38 70; fax: +41 61 688 86 89.
`E-mail address: martin.kuentz@roche.com (M. Kuentz).
`0928-0987/$ – see front matter © 2005 Elsevier B.V. All rights reserved.
`doi:10.1016/j.ejps.2005.08.011
`
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`

`92
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`
`1.
`
`Introduction
`
`A biopharmaceutical assessment of drug substances is crucial
`for different phases of the development process. In an early
`phase, pharmaceutical profiling should help to rate candidate
`molecules in terms of their “druglike” properties (Balbach and
`Korn, 2004; Kerns and Di, 2003). The pivotal challenge is to find
`an appropriate molecule for preclinical and clinical develop-
`ment. Herein a drug categorisation according to the biophar-
`maceutical classification system (BCS) (Amidon et al., 1995) is
`helpful. Once the candidate selection is made, the BCS con-
`cept is also valuable in view of project planning. The choice
`of an adequate clinical formulation scenario is difficult, as it
`has to meet the general project timelines and must take the
`involved risks into consideration. Those risks can be consider-
`able with biopharmaceutically challenging drugs like those in
`BCS class II, III or IV. A straightforward technical development
`could lead to a formulation that does not show adequate expo-
`sure in humans. On the other hand, de-risking, in the form
`of parallel development of sophisticated formulations, is cost
`intensive. This is especially the case for bioavailability test-
`ing in animals. An extensive formulation screening would not
`only bind substantial resources, but is also uncertain in terms
`of its relevance for humans.
`This article presents an additional biopharmaceutical
`assessment program in the preclinical development phase.
`
`Fig. 1 shows when this assessment program should be con-
`ducted in the framework of other formulation activities. The
`assessment directly affects the choice of the clinical develop-
`ment concepts. Depending on the development strategy one
`can assign meaningful development resources about 1 year
`before the first human is dosed.
`The biopharmaceutical assessment program consists of
`two steps. The first step is a computer simulation that should
`help to elucidate the hurdles for oral bioavailability, as well as
`identify critical parameters of a drug formulation. The simula-
`tion may also reveal some parameters that only have a limited
`impact on the biopharmaceutical performance of a formula-
`tion, which is of equal importance. This first step is only an
`in silico model, but the physiology of the human situation is
`simulated. The second part of the biopharmaceutical assess-
`ment is to test proof of concept formulations in statistically
`designed pharmacokinetic experiments. This second step has
`the limitations of the animal model, but this time in vivo data
`are generated.
`The field of computer simulations with physiologically
`based pharmacokinetic absorption models has been covered
`in several articles (Yokoe et al., 2003; Agoram et al., 2001; Nor-
`ris et al., 2000; Plusquellec et al., 1999). Recently, Dannenfelser
`et al. (2004) reported the application of a computer simulation
`for a clinical dosage form development. This profits from a bet-
`ter quality of input data in comparison to the discovery phase
`since a first pharmaceutical profiling including BCS classifica-
`
`Fig. 1 – Gantt chart of the relevant formulation development activities including the new additional biopharmaceutical
`assessment program.
`
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`

`e u r o p e a n j o u r n a l o f p h a r m a c e u t i c a l s c i e n c e s 2 7 ( 2 0 0 6 ) 91–99
`
`93
`
`tion is normally available. The underlying model in Dannen-
`felser’s work as well as in the present paper goes back to the
`compartmental absorption and transit model (CAT) (Yu et al.,
`1996a,b) a more versatile model than the single-tank mixing
`model (Sinko et al., 1991). The CAT model predicts absorption
`through the intestinal tract and takes into account the flow
`of the drug through the digestive tract, which is divided into
`a set of compartments. The model was further developed to
`the so-called advanced CAT (ACAT) model (Agoram et al., 2001)
`and implemented in a commercially available software pack-
`age called GastroPlusTM.
`Based on GastroPlusTM computer simulations a biophar-
`maceutical evaluation can be made as to which factors could
`have an impact on the oral bioavailability. The results should
`lead to the design of experimental formulations. This sec-
`ond step of the proposed strategy should roughly assess the
`difference between a “good formulation” and a “rather poor
`formulation” in vivo using the dog as pharmacokinetic model.
`A recent paper emphasizes the use of design of experiment
`techniques (DoE) to gain maximal information from a mini-
`mal number of animals (Kuentz et al., 2003). It allows in the
`present case to study the effect of the galenical formulation
`on pharmacokinetic parameters depending on the dose and
`feeding status of the animals. The results of using both, com-
`puter simulation and DoE in preclinical bioavailability testing
`are discussed with special emphasis on further planning of
`the clinical formulation activities.
`
`2.
`
`Materials and methods
`
`2.1.
`
`Characterization of the drug substance
`
`R1315 (MW; 409.41 g/mol) is a hydrogen sulphate with a pKa
`value of 5.9, and a c log P of 5.5 (log D7.4 of 4.9). The melting
`point (DSC determination at 5 ◦C/min) displayed an onset of
`241.2 ◦C and a peak at 245.4 ◦C. The solubility of the drug was
`in aqueous systems overall very low (<1 ␮g/mL at pH values
`higher than 5). The drug solubility was also tested in physi-
`ologically relevant media (Galia et al., 1998; Dressman et al.,
`1998; Ingels and Augustijns, 2003). The solubility was high-
`est with 0.2 mg/mL at room temperature in simulated gastric
`fluid (SGF at pH 1.2), whereas in fed simulated standard vehi-
`cle (FeSSIF) 0.1 mg/mL were dissolved at pH 5 and 0.05 mg/mL
`at pH 6.5. In fasted simulated standard vehicle (FaSSIF at pH
`6.5) only 0.002 mg/mL could go into solution. These solubility
`values were determined by HPLC after equilibration for 3 h at
`room temperature.
`The drug exhibited supersaturated solutions in pres-
`ence of mixed micelles. In FeSSIF at pH 5 the initial sol-
`ubility was about 0.6 mg/mL, whereas in FeSSIF at pH 6.5
`roughly 0.3 mg/mL were dissolved in the beginning. The
`initial kinetic solubility is hence approximately six times
`higher than the equilibrium value in FeSSIF. Both solu-
`tions showed after 30 min a bit less than half of the initial
`solubility.
`The drug compound was determined as highly permeable
`in the Parallel Artificial Permeability Assay (PAMPA) (Kansy
`et al., 1998) and was rated as a BCS class II compound.
`
`Table 1 – Fractional 25−2 design for screening of
`excipients in view of R1315 drug solubilisation
`Tween®
`Solutol®
`No. Cremophor®
`RH40 (%)
`20 (%)
`HS 15
`(%)
`
`PVP
`K30
`(%)
`
`Propylene
`glycol (%)
`
`1
`2
`3
`4
`5
`6
`7
`8
`9
`10
`11
`
`3.5
`7
`3.5
`0
`0
`3.5
`7
`7
`0
`7
`0
`
`3.5
`0
`3.5
`7
`0
`3.5
`0
`7
`0
`7
`7
`
`3.5
`7
`3.5
`0
`7
`3.5
`0
`0
`0
`7
`7
`
`2.5
`0
`2.5
`0
`5
`2.5
`0
`5
`5
`5
`0
`
`5
`10
`5
`10
`0
`5
`0
`0
`10
`10
`0
`
`Materials and manufacture of the experimental
`2.2.
`formulations
`
`The drug R1315 was synthesized by the chemical develop-
`ment department of F. Hoffmann-LaRoche Ltd. in Basel. The
`excipients Cremophor® RH40 (polyoxyl 40 hydrogenated cas-
`tor), Solutol® HS 15 (polyethylene-660-hydroxystearate) and
`Kollidon® 30 (polyvinylpyrrolidone, PVP K30) were obtained
`from BASF (Germany), whereas the Tween® 20 (polysorbate 20
`or polyethylene-20-sorbitan-monolaurat) was purchased from
`Fluka (Switzerland). The surfactants are all non-ionic and all
`excipients were used as received.
`The excipients were tested with the drug base of R1315
`in a quarter fraction 25−2 statistical design to achieve max-
`imal drug solubilisation. The different compositions can be
`inferred from Table 1. The finally selected surfactant solution
`contained 5% Cremophor® RH40 with the two concentrations
`of 1 and 2 mg/mL drug base of R1315.
`For the solid dosage form the drug was manually filled in
`hard gelatine capsules of size No. 0 (Capsugel, France). All cap-
`sules were prepared and weighed on the same day of animal
`dosing.
`
`Solubility measurements and analytical HPLC
`
`2.3.
`method
`
`The formulation samples for solubility measurements were
`equilibrated at 25 ± 1 ◦C during 24 h, filtrated (Millipore 0.22 ␮m
`pore size) and consecutively analyzed by HPLC (Waters
`Alliance) with a reversed phase column (Symmetry C18 5 ␮m
`from Waters). The column temperature was 40 ◦C. A gradi-
`ent method was used with a mobile phase consisting of 50%
`50 mM Tris in water at pH 7.0 (adjusted with HCl) and 50% ace-
`tonitrile. The second eluent consisted of 50% 50 mM Tris in
`water at pH 7.0 (adjusted with HCl) and 70% acetonitrile. The
`flow rate was 1 mL/min and UV detection was at 245 nm.
`
`Computer simulation of absorption using
`2.4.
`GastroPlusTM
`
`Computer simulations were performed with the software
`GastroPlusTM V.4.0 (Simulations Plus Inc., Lancaster, USA),
`
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`
`

`

`94
`
`e u r o p e a n j o u r n a l o f p h a r m a c e u t i c a l s c i e n c e s 2 7 ( 2 0 0 6 ) 91–99
`
`which uses a physiologically based absorption model with
`nine compartments corresponding to different segments
`of the digestive tract. GastroPlusTM uses a set of differ-
`ential equations to model the amount of drug that is
`released, dissolved and absorbed for the nine compartments.
`This advanced compartmental absorption and transit model
`(ACAT) is described in Agoram et al. (2001) and further details
`of the computer program are given under the homepage
`http://www.simulations-plus.com/.
`The pH values in the segments can be inferred from the
`literature (e.g. Dressman et al., 1998) and GastroPlusTM V.4 uses
`the following default values for the human fasted condition:
`stomach pH 1.7, duodenum pH 6.0, jejunum pH 6.2–6.4, Ileum
`pH 6.6–7.5 and colon (caecum) pH 5.0.
`Given a known solubility at any single pH, the solubility
`at all other pH values was estimated taking into account the
`pKa value. Fig. 3 shows the example of “lower limit” solubility
`versus pH profile. The profile takes into account the measured
`value in FaSSIF (pH 6.5) as a “worst case” solubility, because it
`is an equilibrium value at room temperature (RT). It is a well
`established approach to set an upper limit for the solubility
`increase as a function of pH. The default value for bases in
`GastroPlusTM is a factor of 50. We used instead of this arbitrary
`factor a cut off solubility measured in SGF at pH 1.2. It is again
`expected that the in vivo solubility at 37 ◦C is higher. Accord-
`ingly, the parameter sensitivity analysis considered additional
`solubility versus pH profiles with solubility values in a range
`of up to 100 times higher than displayed by Fig. 3 to cover a
`broad range of possible in vivo solubilities.
`GastroPlusTM also includes a mean precipitation time. This
`parameter is understood as a mean time for particles to pre-
`cipitate from solution when the local concentration exceeds
`the drug solubility. Usually a default value of 900 s is assumed,
`however, based on the solubility experiments in artificial
`intestinal fluids, a first estimate of 1800 s was set and con-
`sequently a range of 180–18,000 s checked in the parameter
`sensitivity analysis.
`In GastroPlusTM, the dissolution rate constant is given by:
`
`Kd = 3
`
`Cs − Cl
`rT
`
`where is the Noyes–Whitney diffusion coefficient,  the den-
`sity of the drug particle, r the radius, T the diffusion layer
`thickness, Cs the solubility and Cl is the lumen concentration.
`The diffusion layer thickness was taken as equal to the particle
`radius.
`
`The relevant input and GastroPlusTM default parameters
`are presented in Table 2. Most of the compound parameters
`for R1315 were directly obtained from the preformulation data.
`The permeability result from the PAMPA assay was converted
`to an effective human jejunal permeability, Peff based on a
`correlation built with 20 reference drugs (Roche in-house ref-
`erence). It was assumed for R1315 that no carrier-mediated
`transport mechanism is involved.
`Theoretically, the absorption rate coefficient, ka(i) for each
`compartment is the product of the effective permeability
`value, Peff(i) for the compartment and the absorption scale
`factor, ˛(i) for the compartment. This absorption scale fac-
`tor is in theory simply the ratio of the surface area to vol-
`ume, which reduces to 2/R, where R is the radius of the
`compartment. Since the individual effective permeability val-
`ues are unknown for the different compartments the usual
`GastroPlusTM practice was followed to use only the one
`estimate for the effective jejunum human permeability as
`described above. Accordingly, the absorption scaling factors
`were adjusted using software default values based on the
`so-called log D model. This model considers the influence
`of the log D on the effective permeability. In other words,
`as the ionised fraction of a compound increases the effec-
`tive permeability decreases. GastroPlusTM includes further
`default values for the transit times in the human GI tract,
`which enables to predict the rate and extent of drug absorp-
`tion.
`For simulation of plasma concentrations some estimates
`of human pharmacokinetic parameters are required. It was
`known for this compound that for three preclinical species in
`vitro data obtained with primary cultures of hepatocytes gave
`reasonable estimates of the observed in vivo clearance. There-
`fore, human clearance was estimated from intrinsic clear-
`ance measured in human hepatocytes and scaled to in vivo
`using physiologically based principles (Zuegge et al., 2001).
`The estimated human hepatic clearance was 0.15 L/(h kg).
`From this clearance value, assuming a liver blood flow of
`1.2 L/(h kg) and a blood/plasma ratio of unity, 12.5% of the
`drug was estimated to be extracted on first pass through the
`liver.
`Simulations of absorption following dosing of 160 mg of an
`immediate release form of R1315 were performed and also at
`double this dose. Then a parameter sensitivity analysis was
`performed where the particle size and drug solubility values
`were varied and their impact on the oral bioavailability ascer-
`tained.
`
`Table 2 – Input variables and accepted default values for the GastroPlusTM simulation
`
`General simulation and compound parameters
`
`Physiological parameters
`
`Pharmacokinetics
`
`MW: 409.41 g/mol
`c log P: 5.5
`pKa: 5.9
`Dosage form; immediate release capsule with 160 mg dose
`Lower limit reference solubility (pH 6.5) 0.002 mg/mL
`Mean precipitation time: 1800 s
`Particle density: 1.2 g/mL
`Effective permeability: 4.4 × 10−4 cm s−1
`Effective particle radius: 5 ␮m
`
`Human fasted conditions
`Absorption model: log D model
`Stomach transit time: 0.1 h
`Dose volume: 250 mL
`Small intestine transit time: 3.3 h
`Small intestine radius: 1.2 cm
`Small intestine length: 300 cm
`Colon volume: 1200 mL
`–
`
`Body weight: 70 kg
`First pass extraction (fixed): 12.5%
`Blood to plasma concentration ratio = 1
`Clearance: 0.15 L/(h kg)
`Vc: 1.9 L/kg
`–
`–
`–
`–
`
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`
`95
`
`Table 3 – 23 design for dog pharmacokinetic studies
`
`No.
`
`Formulation
`
`Dose level
`(mg/kg)
`
`Feed status
`
`1
`2
`3
`4
`5
`6
`7
`8
`
`Capsule
`Capsule
`Solution
`Capsule
`Solution
`Solution
`Capsule
`Solution
`
`2
`2
`2
`4
`4
`2
`4
`4
`
`Fed
`Fasted
`Fasted
`Fed
`Fasted
`Fed
`Fasted
`Fed
`
`2.5.
`
`Pharmacokinetic experiments in beagle dogs
`
`Male beagle dogs (weighing 10–14 kg, obtained from the Roche
`in-house colony) were used for the oral bioavailability stud-
`ies. The dosing was performed according to a randomized 23
`factorial design outlined in Table 3. Two doses 2 and 4 mg/kg
`were administered orally as drug in a capsule as well as a drug
`solution under both, fed and fasted conditions. In the fed con-
`dition, the animals received 200 g commercially available dog
`food 30 min prior to the administration of the test article. In the
`fasted condition, food was withdrawn overnight before dosing
`and was not given during the experiment.
`Blood samples (approximately 1 mL each) were drawn from
`the cephalic vein at predose, and 0.5, 1, 1.5, 2, 3, 4, 6, 8 and
`24 h postdose. Collection tubes contained EDTA as antico-
`agulant. Plasma was obtained by centrifugation and stored
`deep-frozen at approximately −20 ◦C until analysis. A selective
`LC–MS/MS method was used for the quantification of R1315.
`The drug R1315 and an internal standard were isolated from
`plasma samples after protein precipitation by on-line solid
`phase extraction and separated from other constituents of the
`sample by narrow-bore HPLC. Detection was accomplished
`utilising ion spray MS/MS in positive ion selected reaction
`monitoring mode. The limit of quantification was 1 ng/mL. The
`assay performance was monitored using quality control sam-
`ples spiked with known concentrations of R1315.
`AUC0-inf (area under the plasma concentration–time curve)
`was estimated by non-compartmental analysis (applying the
`linear trapezoidal rule), using the pharmacokinetic evaluation
`program WinNonlin Pro®. Extrapolation to infinity was per-
`formed from the last measurable concentration using the rate
`constant of the apparent terminal plasma level profile.
`
`Fig. 2 – Standardised effects of solubility enhancement for
`R1315 as Pareto chart.
`
`and so a degree of freedom of five was obtained for the esti-
`mate of the total error.
`A Pareto chart was selected to rank the estimated effects in
`decreasing order of magnitude (Fig. 2). The length of each bar
`is proportional to the standardised effect. This standardised
`effect is the estimated effect divided by its standard error. The
`vertical line of the plot marks those effects that are statisti-
`cally significant. Bars that extend beyond the line correspond
`to effects that are statistically significant at the 95% confidence
`level.
`Screening designs were proposed before for preclinical
`tests in animals (Kuentz et al., 2003). In the current study, a 23
`full factorial design was conducted in a single block. The effect
`of the formulation, the clinical dose, as well as the food effect
`was evaluated in parallel. The full factorial design allowed fur-
`ther to estimate the interactions of the different factors (none
`of the effects or first order interactions are confounded corre-
`sponding to a resolution V). This detection of interactions is
`an advantage of the full factorial design that has on the other
`hand here only a rather low degree of freedom (d.f.) for estima-
`tion of the total error, so that the statistical power of this study
`plan is rather low. The evaluation was therefore conducted on
`a 90% significance level to obtain an acceptable probability for
`ˇ. The calculated power curve shows that a true effect would
`have to be at least seven to eight times greater than its stan-
`dard deviation to have adequate probability of effect detection.
`Thus, only marked effects can show significance in this design.
`The statistical evaluations were calculated using the software
`package Stagraphics® Plus V. 5.0 (Manugistics Inc.).
`
`Statistical design of experiment techniques and
`2.6.
`evaluation of the data
`
`3.1.
`
`Development of experimental formulations
`
`3.
`
`Results and discussion
`
`There are introductions to the field of planning factorial or
`fractional factorial designs (Kleppmann, 2003; Lewis et al.,
`1999). Senderak et al. (1993) was one of the pioneers to use
`design of experiment techniques to optimize drug solubility
`in an oral solution. In the present study, a quarter fraction 25−2
`factorial design was used for screening purposes. The effect of
`three surfactants, a polymer and a co-solvent in view of drug
`solubilisation was evaluated. The compositions of the eleven
`mixtures are displayed by Table 1. Since the design has only a
`resolution of III, the main effects are confounded with interac-
`tions. These interactions between two factors were neglected
`
`The development of a drug solution exhibiting maximal solu-
`bility was carried out as a part of the toxicological formulation
`development. Initial solubilisation studies used the drug base.
`Different surfactants were evaluated regarding their poten-
`tial to get the best drug solubility. Fig. 2 shows the effects of
`the different excipients on drug solubilisation. The presence
`of Cremophor® RH40 enabled highest drug concentrations in
`solution, whereas Solutol® HS15 and Tween® 20 displayed
`lower solubilisation capacities. The three non-ionic surfac-
`tants incorporate the drug mainly in micelles. The extent of
`solubilisation is affected by the nature of the surfactant as well
`
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`

`96
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`e u r o p e a n j o u r n a l o f p h a r m a c e u t i c a l s c i e n c e s 2 7 ( 2 0 0 6 ) 91–99
`
`the complexity of the GastroPlusTM model and the numerous
`input data some care is needed in interpretation of results.
`Our usual strategy with GastroPlusTM is therefore to verify the
`simulations against in vivo results in one or more preclini-
`cal species before making prospective simulations in human.
`In this study, checks on the reasonableness of predicted oral
`absorption in rat (in terms of consistent plasma profiles; data
`not shown) were made before proceeding with the human
`simulations and sensitivity analysis. The timelines in Fig. 1
`show that these modelling and simulation activities using ani-
`mal in vivo data precede the biopharmaceutical assessment
`program described in this article.
`The results of the first human simulation with a single
`immediate release dose of 160 mg R1315 are shown in Fig. 4.
`A fraction of the dose goes into solution in the acidic environ-
`ment of the stomach and there is later a partial precipitation of
`drug base in the intestine. However, the precipitated drug has
`sufficient time to be re-dissolved and is well absorbed due to
`the high permeability of the drug. Accordingly, the simulation
`implies that stomach solubility of the drug has little impact
`on the overall fraction absorbed. The drug permeation and re-
`dissolution are overlapping processes with the fast removal
`of drug from the intestine creating a constant sink and pro-
`moting further base to go into solution. The fraction absorbed
`is high, and the oral bioavailability is slightly reduced by liver
`metabolism during the first pass.
`The simulation was repeated with a doubled dose of 320 mg
`R1315. Fig. 5 shows that at this higher dose level there is a
`lower fraction of the dose dissolved in the stomach. The full
`dissolution step, involving also precipitated drug, takes more
`time than observed with the lower dose, but the GI transit time
`is sufficient and so the bioavailability values for both doses are
`very similar in simulations over 24 h.
`In a sensitivity analysis, the pharmaceutically relevant
`parameters of mean particle size and drug solubility were
`taken as variables over a 100-fold range (at a dose of 160 mg
`R1315). The predicted dependence of oral bioavailability on
`reference solubility at pH 6.5 (assuming shifted solubility ver-
`sus pH profiles similar to Fig. 3 is shown in Fig. 6. The oral
`
`as of that of the drug. Within a homologues series of a given
`surfactant one may state as a rule of thumb that the solubilisa-
`tion capacity of a hydrophobic drug increases with the length
`of the hydrocarbon chain and the number of ethylene oxide
`units. The latter effect is mainly due to an increased number of
`micelles per mole (Florence and Attwood, 1998). Certainly, the
`comparison of structurally very different surfactants is more
`difficult, which makes an experimental ranking indispensable.
`The polymer PVP K30 showed a slight, but significant effect
`on drug solubilisation, while this was interestingly not the
`case for the propylene glycol. The lacking co-solvent effect
`must be understood within the concentration range exam-
`ined. It should be further highlighted that interactions of
`the excipients were not considered in the evaluation of the
`25−2 screening design. The effect of the co-solvent was esti-
`mated from mixtures with surfactants that predominantly
`solubilised the drug. The involved micelle formation could be
`hindered by the presence of propylene glycol, which can be
`supported by thermodynamic arguments. The driving force
`of micelle formation lies in a positive entropy change aris-
`ing from the release of structured water that occurs when
`hydrophobic surfactant chains form the core of a micelle
`(Martin, 1993). The positive entropy change should therefore
`be highest in pure water systems, whereas aqueous solutions
`of propylene glycol are likely to exhibit a less pronounced
`entropy change. In essence, a co-solvent can have a nega-
`tive interaction with surfactants that solubilise the drug by
`micellation. This is reflected by the results showing that a
`combination of surfactant with co-solvent does not increase
`solubilisation of R1315 and so Cremophor® RH40 was used
`alone with the drug in the final experimental formulation.
`Based on the obtained results different solutions with vary-
`ing amounts of Cremophor® RH40 were tested in view of sol-
`ubilising at least 0.2% (w/w) R1315. Finally, an aqueous 5%
`(w/w) Cremophor® RH40 vehicle was used to prepare a 1 and
`2 mg/mL solution of R1315. Both solutions contained 0.18%
`methylparaben and 0.02% proylparaben as preservatives. An
`HPLC analysis of the drug solution was performed directly
`after the manufacture and following 10 days storage at both 5
`and 25 ◦C. The concentrated drug solution showed an increase
`of the total degradation products of +0.36% stored at 5 ◦C, and
`+2.17% at 25 ◦C, respectively. A daily ad hoc preparation was
`decided for this type of experimental drug solution to avoid
`any stability issue.
`This first experimental formulation is favourable from a
`biopharmaceutical point of view over formulations, where the
`drug is crystalline. The extreme case was represented by the
`experimental formulation of pure drug substance in a capsule,
`since there is no granulation step or additional excipients that
`could improve the poor wetability of R1315. Stability tests were
`conducted with the solid drug R1315 at different temperatures
`and humidities. No degradation could be detected following
`1 week storage at 40 ◦C/75% R.H., which indicates sufficient
`chemical stability at least for an ad hoc usage of the drug.
`
`Results from the physiologically based computer
`3.2.
`simulation
`
`The validity of any computer modelling is dependent on the
`quality of both the model and of the input data. In view of
`
`Fig. 3 – Drug solubility vs. pH profile used as lower limit of
`the solubility values of a set of computer simulations.
`
`Apotex v. Cellgene - IPR2023-00512
`Petitioner Apotex Exhibit 1044-0006
`
`

`

`e u r o p e a n j o u r n a l o f p h a r m a c e u t i c a l s c i e n c e s 2 7 ( 2 0 0 6 ) 91–99
`
`97
`
`Fig. 4 – Simulated drug amounts (%) for the 160 mg dose of
`R1315 in human.
`
`Fig. 5 – Simulated drug amounts (%) for the 320 mg dose of
`R1315 in human.
`
`bioavailability was hardly affected by reference solubility val-
`ues (pH 6.5) in the range of 0.002–0.2 mg/mL. Thus, higher
`solubility values may increase the rate of absorption, but not
`its extent. We further repeated single simulations at a 160
`and 320 mg dose at 0.02 and 0.2 mg/mL reference solubility
`to study the dissolution and absorption process analogues
`to Figs. 4 and 5. The amount of dissolved drug was greatly
`increased and complete absorption was reached at an earlier
`time point so that almost no colonic absorption was predicted.
`This is different from the results at the lower reference solu-
`bility 0.002 mg/mL (Figs. 4 and 5) where absorption in the colon
`played a role.
`The parameter sensitivity analysis was repeated with vary-
`ing mean effective particle sizes ranging from 0.5 to 50.0 ␮m.
`A similar bioavailability prediction was obtained with the dif-
`ferent particle sizes (Fig. 6). However, there is a tendency
`towards slightly reduced bioavailability at the lower drug sol-
`ubility of 0.002 mg/mL and the upper 50.0 ␮m particle size, but
`within the examined ranges the bioavailability was not greatly
`affected. The parameter sensitivity analysis was repeated with
`a doubled dose of 320 mg R1315. A similar picture was observed
`as displayed for the lower dose by Fig. 6. We further varied the
`precipitation time between 180 and 18′000 s at a dose of 320 mg
`and found again that oral bioavailability was hardly affected.
`This indicates that even at a relatively fast precipitation of
`drug, the re-dissolution and permeation are fast enough to
`make the given dose bioavailable.
`The set of simulations was completed by varying the effec-
`tive permeability from 0.44 to 44 × 10−4 cm s−1. A nearly con-
`stant bioavailability was predicted with a slight decrease at
`the low