Correlations between in vitro and in vivo data (IVIVC) are often used during pharmaceutical development in order to reduce development time and optimize the. This presentation gives a bird’s eye view on Dissolution in context with IVIVC. It discusses various levels of Correlations currently in practice. Invitro Invivo study & their correlation shortens the drug development period, economizes the resources and leads to improved product quality. Increased activity.
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Different batches of atorvastatin, represented by two immediate release formulation designs, were studied using a novel dynamic dissolution apparatus, simulating stomach and small intestine.
A universal dissolution method was employed which simulated the physiology of human gastrointestinal tract, including the precise chyme transit behavior and biorelevant conditions. The multicompartmental dissolution data allowed direct observation and qualitative discrimination of the differences resulting from highly pH dependent dissolution behavior of the tested batches.
While satisfactory correlation could not be achieved using a conventional deconvolution based-model, promising results were cofrelation through the use of a nonconventional approach exploiting the complex compartmental dissolution data. An orally administered drug has to be released from its dosage form, dissolved in the surrounding fluid and absorbed by the gut wall, in order to enter the blood stream.
Dissolution testing in pharmacy studies the first two processes and is not only a vital tool for assessment of quality of a pharmaceutical, but also a tool for elucidation and simulation of these effects in vitro.
The research in drug development facilitates dissolution to uncover and predict many crucial aspects influencing the fate of an administered active pharmaceutical ingredient API in the gastrointestinal tract GITwhile employing a wide variety of innovative and special apparatuses, either based on the conventional pharmacopeial tools or having a completely original design [ 1 ].
Such instruments iciv involve only one or two compartments, and they functionally often address only a specific focus of a study, that is, drug precipitation, mechanical qualities of a dosage form, and so forth [ 2 ].
However, a more corfelation simulation of the GIT is often needed, and currently only TIM-1 apparatus fully enables in vitro testing in completely biorelevant conditions ranging from stomach to ileum [ 3 ]. This paper discusses the application and evaluation of this apparatus, performed with several generic and reference batches of iiviv release tablets containing atorvastatin ATV.
This drug was chosen due to the availability of in vivo data from subsequent bioequivalence BE studies for the tested formulations. But being correlatiion drug of limited bioavailability, due to high variability in first-pass metabolism, makes it difficult to correlate correation conventional in vitro tests results with blood concentration in time or other pharmacokinetic parameters. The instrument is a computer controlled artificial digestive tract, designed for dynamic dissolution testing of oral dosage forms and consisting of four compartments: Physiological conditions are maintained in the system with the possibility of adjusting all method parameters, for example, pH, volumes, transit times, temperature, and so forth.
The dissolution in fed iciv can be tested with liquid meal e. The transport of chyme in Golem is driven by peristaltic pumps placed between each two subsequent compartments, where the fourth pump leads the chyme from the ileum into the collection canister waste.
The compartments are made from modified common intravenous bags the plastic was tested for interaction with various APIs. The bags involve three ports: Peristaltic movement is simulated by a V-shaped grate, pressed down on the bags, which rocks from side to side, driven by compressed air. The operation of the apparatus requires one person for manual collection of samples and injection of enzymes, using common syringes. From perspective of biorelevancy, the Golem apparatus is one of the three most complex dissolution apparatuses described to date other two being TIM-1 and Dynamic Gastric Model [ 3 ].
Its main advantage is that the complex functions are based on simple technical solutions which make the apparatus user friendly and easy to modify, and at the same time significantly less expensive than the alternatives.
A method simulating fasted state and developed according to previous work was used in the study [ 6 ]. The experiments were started directly after insertion of a dosage form into the first stomach compartment. The oviv medium containing both the dissolved and undissolved contents of the dosage form was gradually moved by the peristaltic pump from stomach into the duodenal compartment and then into the following two compartments an approximate scheme of chyme transfer in Golem is depicted in Figure 2.
The medium thus underwent a dynamic change as it merged with the contents starting volumes of the next compartments.
The starting dissolution medium was based on a physiological solution with pH modified by hydrochloric acid or bicarbonate buffer; pepsin was added only to the gastric compartment. The pH values, concentration of bile salts bile extract porcine, Sigma-Aldrichand lipase pancreatin, Zentiva in the SI compartments were maintained at steady levels throughout the experiment. The pH in stomach compartment was left to change, being influenced by the studied formulation.
The detailed information on the method design is given in Table 1. The withdrawal of the medium and dissolved API was considered in the calculations. From the generic batches, four contained amorphous form of the drug batches correlatipn, 82, 01, and 02which had higher intrinsic dissolution rate compared with the crystal form contained in the generic batch80 and all the reference batches Lipitor, Sortis06, Sortis10 [ 10 ].
All corrflation with the crystal ATV also contained CaCO 3 as a buffering agent used to raise the gastric pH and thus facilitate very early dissolution of ATV, which is a weak acid almost insoluble in pH below 4.
The amorphous generic batches, on the other hand, contained no buffer, which was to slow down the faster dissolution of the amorphous form of the drug. The BE studies of the five generic formulations were evaluated in correlatiob crossover PK studies, performed by other contract workplaces: In order to establish a relationship between dissolution test results and actual results from in vivo studies, different ocrrelation approaches were applied.
A conventional method was based on a numeric deconvolution, where whole in vitro profiles of cumulative fraction dissolved were taken into account, with or without exclusion of the measurements from selected compartments. The statistical computing software was used for modeling.
In order to calculate fraction of drug absorbed in time, simulated in vivo profile i. Simulation of in vivo profile after i. Volume of distribution was calculated with use of the model based upon a modified version of the Poulin and Theil method [ 13 ].
Input information covered system ivkv specific for the chosen population as provided by simulatortrial design information single 30 seconds long viiv. Physicochemical, binding, and ADME absorption, distribution, metabolism, and excretion data are presented in Table 2. Renal clearance of ATV was assumed to be negligible [ 14 ].
A ivjv approach was based on direct scaling of the outcome correltaion tests carried on Golem apparatus, where particular compartments were correlated with in vivo profiles. Such an approach is common when sustained-release dosage forms are tested but becomes problematic with immediate-release formulations IR.
All batches of the model drug, atorvastatin, were of immediate release and in the case of either the buffered or the coerelation formulations were behaving similarly. Therefore, external predictability and validation were evaluated when one formulation was characterized by medium rate release kinetics, used as testing formulation, and two other formulations were used for model building.
In case of internal validation, testing formulation was included in model building phase.
In vitro – in vivo correlation: from theory to applications.
Whole or partial area under the curve pAUC prediction error PE 1 and prediction error 2 was calculated [ 15 ]: The particular BE study results were as follows: The average plasma concentration-time profiles are plotted together in Figure 3. The corfelation important origin of the variability seems to be the gut and liver correation of the drug [ 16 ]. An important observation from the average Correlarion curves is the very short values 0.
The Golem dissolution experiments were started with batch80 and its reference product Lipitor, since both products contained the same crystal form of ATV, and both were buffered by CaCO 3as excipient. Both formulations showed almost parallel dissolution profiles, but with higher dissolved amount in case of batch80, which qualitatively corresponded with the in vivo results.
The further tested buffered formulations included two reference batches from the BE studies Lipitor and Sortis10plus one other batch of the reference product, Sortis All three reference batches showed almost identical dissolution behavior see Figure 5which confirmed that they were interchangeable in the dissolution experiments. In contrast, all correlatiob formulations, generic and reference alike, raised the ivuv pH towhich enabled rapid dissolution of ATV.
This observed difference between the buffered and nonbuffered formulation in Golem provided a useful hint on the manner of in vivo dissolution behavior.
As for correlatioon nonbuffered batches, these generally provided higher fraction dissolved as they did not contain the CaCO 3 buffering agent. Other than that, the batch85 with low bioavailability in vivo had the lowest dissolution performance, followed by the bioequivalent batch82, and corrleation the two batches with highest bioavailability—batch01 and batch However, the dissolution profiles for batches 01 and 02 were identical, and, according to similarity and difference factor, they also did not differ from the profile of batch Moreover, API powder which viiv as a control also showed identical dissolution performance to batches 01 and 02, indicating that the dissolution method lacked discriminatory power for these highly dissolving batches.
This was caused by low medium volume although physiological in combination with lack of an absorption step sink-condition. Nevertheless, the dissolution method was developed as a universal, most physiologically relevant simulation of fasted state, and as such it provided results beyond expectation. The obvious and simple option for improvement of dissolution testing of drugs with low solubility such as atorvastatin would be modification of the method by employing higher unphysiological medium volumes in the individual compartments, which would prevent early saturation of medium with the tested API.
Of course, certain very lipophilic drugs would require unachievably high medium volumes to enable full dissolution of the administered dose. It is however questionable whether this is necessary when considering the fact that such drugs would neither be expected to corre,ation dissolve in vivo.
However, study of coreelation effects was beyond the scope of the present study.
IVIVC – Wikipedia
The results depicted below follow a general agenda of in vitro – in vivo correlation level A, where fraction absorbed in vivo is directly correlated to the fraction dissolved in vitro. A linear function is preferred for this correlation, although other reasonable mathematical relationships are allowed when properly justified.
The key procedure here was a deconvolution of a PK profile into the cumulative curve describing fraction absorbed in vivo. Since atorvastatin PK p. Instead, more sophisticated numerical deconvolution was employed.
Numerical deconvolution relies on the availability of a PK profile of drug ccorrelation intravenously i. Since in the presented case, bioassay protocol did not corrlation such atorvastatin administration, an approximation of i.
In case of the dissolution results available, the presence or absence of the buffering agent affected whether drug did dissolve in the stomach compartment, which produced two distinct cumulative profile patterns and significantly limited the comparison of the two formulation designs.
In vivo absorption of atorvastatin is believed to start only in small intestine and thus a deconvolution of plasmatic profile would yield a fraction of drug absorbed only from small intestine. According to this, the fraction of drug dissolved in stomach compartment was excluded correlarion the cumulative profiles used for the IVIV correlations.
The final profiles used for correlation are depicted in Figure 6. Despite the previous modification of the profiles, the lower solubility of ATV in the presence of CaCO 3 and the resulting overall iviiv dissolution performance correlaion the buffered batches caused that it was difficult to build a reliable model using combination of buffered and nonbuffered formulations.
Therefore, models had to be built for Design I buffered batches and Design II nonbuffered separately. Figure 7 depicts deconvolution results for two formulations of both designs and the lack satisfactory level of correlation. In vitro – in vivo correlation was described by both linear and nonlinear polynomial relationship 3. The lm base function was used: In the nonbuffered group a model was built based on batch01 and batch85 and nonlinear polynomial correlation was obtained with Figure 9.
Predictability evaluation was performed by calculating fraction of ATV absorbed versus time with regression equation with in vitro profile as an input. With intravenous response profile, numerical convolution was performed, and the prediction of in vivo curve was obtained.
Figure 10 depicts simulation of in vivo profile of batch Deconvolution results for buffered batches are presented in Figures 11 and In most cases, nonlinear correlation was observed.
In Figure 13prediction of Sortis10 plasma concentration is shown. An example of internal predictability is presented in Figure In general, predictions of were good, but the descending curves could not properly reflect the in vivo behavior resulting in high AUC PE. This was partially due to the fact that the simulated i. This implies no more requirements for i.