Dr Reddy's Laboratories Ltd. developed generic version of XYZ extended release tablets (ER) and achieved bioequivalence as per criteria mentioned by USFDA in both fasting and fed conditions for higher strength formulation (1200 mg). However, on comparison of multimedia dissolution profiles in pH 4.5 acetate media, the f2 similarity value was <50. The lower strength formulation (600 mg) demonstrated faster dissolution profile. This was identified as strength-dependent sink condition difference and in vitro multiunit dissolution studies were used to justify sink differences between the higher and lower strengths. Additionally, a Physiologically Based Biopharmaceutics Model (PBBM) was developed using GastroPlusTM. The validity of this model was established using in-house human pharmacokinetic data. Further, this model was used to justify the insignificant in vivo impact of the faster dissolution profile for the lower strength formulation. This work provides a novel and less explored approach that can be used to obtain biowaiver for lower strength formulations when the standard biowaiver criteria cannot be met. This work also demonstrates the usefulness of PBBM to justify dissolution dissimilarity between dose proportional formulations and to evaluate its biopharmaceutics risk without the need for actual in vivo studies.

Probabilistic modeling using influence networks is an efficient, intuitive, and easy to communicate strategy in the development of complex pharmaceutical products. This study was aimed to use a risk-based approach to explore the complex interactions between product and process design parameters affecting size and shape of the particles in injectable aqueous crystalline suspensions (ACS). Based on a risk assessment, a design of experiments (DOE) was applied to evaluate the most important parameters, i.e., four critical material attributes and two critical process parameters. A model hydrophobic drug (carbamazepine) was milled and homogenized in a multistep process (dispersion and milling steps). The final formulations were characterized with automated at-line image analysis of thousands of individual particles. The particle size and shape distributions were summarized with descriptive parameters, and the relationship of these parameters and the DOE was modeled using influence networks (INs). This approach was compared and contrasted with a classical modeling approach based on multivariate linear regression (MVLR). INs had a superior visual interpretation capability of the complex and multivariate ACS system making the risk-based decision making more accessible. The probability and causality were included in the IN, i.e., the relationships between size and shape. Moreover, IN allowed to incorporate prior knowledge in a systematic way by implementing a ‘black and white list’. An IN based model was created with the following model performance: a mean absolute percentage error of 1.7% and 1.1% for the size and 6.2% and 5.0% for the shape, respectively for dispersed and milled ACS. Parameters with the highest and lowest probability to control the critical quality attributes of ACS could be identified. Consequently, the parameter settings giving the optimum particle size and shape could be predicted using a Monte Carlo simulation to calculate the probability of success including the uncertainty of the model. The cubic MVLR model for the size of milled ACS was comparable to the IN in terms of the mean absolute percentage error, i.e., 1.1%. However, IN was more efficient in visualizing the complex and multivariate data set, including all the critical quality attributes and formulation/process parameters of the ACS at the same time. Moreover, the prior knowledge used in probabilistic modeling of IN could be systematically documented.

Solid form diversity of raw materials can be critical for the performance of the final drug product. In this study, Raman spectroscopy, image analysis and combined Raman and image analysis were utilized to characterize the solid form composition of a particulate raw material. Raman spectroscopy provides chemical information and is complementary to the physical information provided by image analysis. To demonstrate this approach, binary mixtures of two solid forms of carbamazepine with a distinct shape, an anhydrate (prism shaped) and a dihydrate (needle shaped), were characterized at an individual particle level. Partial least squares discriminant analysis classification models were developed and tested with known, gravimetrically mixed test samples, followed by analysis of unknown, commercially supplied carbamazepine raw material samples. Classification of several thousands of particles was performed, and it was observed that with the known binary mixtures, the minimum number of particles needed for the combined Raman spectroscopy – image analysis classification model was approximately 100 particles per solid form. The carbamazepine anhydrate and dihydrate particles were detected and classified with a classification error of 1 % using the combined model. Further, this approach allowed the identification of raw material solid form impurity in unknown raw material samples. Simultaneous automated image analysis and Raman spectroscopy of powders at an individual particle level has its potential in accurate detection of low amounts of unwanted solid forms in particulate raw material samples.

Abstract. Stable high-molecular-weight esters are present in α-pinene ozonolysis secondary organic aerosol (SOA) with the two most abundant ones corresponding to a hydroxypinonyl ester of cis-pinic acid with a molecular weight (MW) of 368 (C19H28O7) and a diaterpenylic ester of cis-pinic acid with a MW of 358 (C17H26O8). However, their molecular structures are not completely elucidated and their relationship with highly oxygenated molecules (HOMs) in the gas phase is still unclear. In this study, liquid chromatography in combination with positive ion electrospray ionization mass spectrometry has been performed on high-molecular-weight esters present in α-pinene ozonolysis SOA with and without derivatization into methyl esters. Unambiguous evidence could be obtained for the molecular structure of the MW 368 ester in that it corresponds to an ester of cis-pinic acid where the carboxyl substituent of the dimethylcyclobutane ring and not the methylcarboxyl substituent is esterified with 7-hydroxypinonic acid. The same linkage was already proposed in previous work for the MW 358 ester (Yasmeen et al., 2010), but could be supported in the present study. Guided by the molecular structures of these stable esters, we propose a formation mechanism from gas-phase HOMs that takes into account the formation of an unstable C19H28O11 product, which is detected as a major species in α-pinene ozonolysis experiments as well as in the pristine forest atmosphere by chemical ionization–atmospheric pressure ionization–time-of-flight mass spectrometry with nitrate clustering (Ehn et al., 2012, 2014). It is suggested that an acyl peroxy radical related to cis-pinic acid (RO2⚫) and an alkoxy radical related to 7- or 5-hydroxypinonic acid (R′O⚫) serve as key gas-phase radicals and combine according to a RO2 + R′O⚫ → RO3R′ radical termination reaction. Subsequently, the unstable C19H28O11 HOM species decompose through the loss of oxygen or ketene from the inner part containing a labile trioxide function and the conversion of the unstable acyl hydroperoxide groups to carboxyl groups, resulting in stable esters with a molecular composition of C19H28O7 (MW 368) and C17H26O8 (MW 358), respectively. The proposed mechanism is supported by several observations reported in the literature. On the basis of the indirect evidence presented in this study, we hypothesize that RO2 + R′O⚫ → RO3R′ chemistry is at the underlying molecular basis of high-molecular-weight ester formation upon α-pinene ozonolysis and may thus be of importance for new particle formation and growth in pristine forested environments.

The release of drugs from poly(lactic-co-glycolic acid) (PLGA) microparticles depends to a large extent on the porosity of the particles. Therefore, porosity determination of PLGA microparticles is extremely important during pharmaceutical product development. Currently, mercury intrusion porosimetry (MIP) is widely used despite its disadvantages, such as the need for a large amount of sample (several hundreds of milligrams) and residual toxic waste. Here, we present a method based on the estimation of the volume of a known mass (a few milligrams) of particles using micro-flow imaging (MFI) to determine microparticle batch porosity. Factors that are critical for the accuracy of this method (i.e., density of the suspending fluid, particle concentration, and postsample rinsing) were identified and measures were taken to minimize potential errors. The validity of the optimized method was confirmed by using nonporous polymethylmethacrylate microparticles. Finally, the method was employed for the analysis of 7 different PLGA microparticle batches with various porosities (4.0%-51.9%) and drug loadings (0%-38%). Obtained porosity values were in excellent agreement with the MIP-derived porosities. Altogether, the developed MFI-based method is a valuable tool for deriving the total volume of a known mass of PLGA particles and therewith their porosity.

The purpose of this study was to explore the potential of flow imaging microscopy to measure particle size and agglomeration of poly(lactic-co-glycolic acid) (PLGA) microparticles. The particle size distribution of pharmaceutical PLGA microparticle products is routinely determined with laser diffraction. In our study, we performed a unique side-by-side comparison between MFI 5100 (flow imaging microscopy) and Mastersizer 2000 (laser diffraction) for the particle size analysis of two commercial PLGA microparticle products, i.e., Risperdal Consta and Sandostatin LAR. Both techniques gave similar results regarding the number and volume percentage of the main particle population (28–220μm for Risperdal Consta; 16–124μm for Sandostatin LAR). MFI additionally detected a ‘fines’ population (<28μm for Risperdal Consta; <16μm for Sandostatin LAR), which was overlooked by Mastersizer. Moreover, MFI was able to split the main population into ‘monospheres’ and ‘agglomerates’ based on particle morphology, and count the number of particles in each sub-population. Finally, we presented how MFI can be applied in process development of risperidone PLGA microparticles and to monitor the physical stability of Sandostatin LAR. These case studies showed that MFI provides insight into the effect of different process steps on the number, size and morphology of fines, monospheres and agglomerates as well as the extent of microparticle agglomeration after reconstitution. This can be particularly important for the suspendability, injectability and release kinetics of PLGA microparticles.

PurposeTo investigate whether particle sedimentation velocity tracking using a flow imaging microscope (FlowCAM) can be used to determine microparticle porosity.MethodsTwo different methods were explored. In the first method the sedimentation rate of microparticles was tracked in suspending media with different densities. The porosity was calculated from the average apparent density of the particles derived by inter- or extrapolation to the density of a suspending medium in which the sedimentation velocity was zero. In the second method, the microparticle size and sedimentation velocity in one suspending fluid were used to calculate the density and porosity of individual particles by using the Stokes’ law of sedimentation.ResultsPolystyrene beads of different sizes were used for the development, optimization and validation of the methods. For both methods we found porosity values that were in excellent agreement with the expected values. Both methods were applied to determine the porosity of three PLGA microparticle batches with different porosities (between about 4 and 52%). With both methods we obtained microparticle porosity values similar to those obtained by mercury intrusion porosimetry.ConclusionsWe developed two methods to determine average microparticle density and porosity by sedimentation velocity tracking, using only a few milligrams of powder.

Polymeric micelles are nanoparticles formed upon self-assembly of amphiphilic (block co-)polymers in aqueous solutions. The resulting structure is a usually spherical nanoparticle with a hydrophobic core acting as a reservoir for poorly soluble active pharmaceutical ingredients (APIs) and a hydrophilic shell which provides colloidal stability and limits protein adsorption and opsonisation, resulting in long-circulation times. Since the physicochemical properties, and ultimately the in vivo distribution, safety and efficacy, of the final drug product are highly dependent on the chosen polymer chemistry and manufacturing process, classification of polymeric micelles as nonbiological complex drugs is justified. This chapter provides an overview of the most important/common chemistry, manufacturing processes and control strategies used to manufacture polymeric micelles for medicinal products. Next, the pharmacology of polymeric micelles tested in the clinic is summarized and the relation between physicochemical characteristics and PK/PD as well as evaluation of choice and value of specific PK-parameters and required assay development are discussed. Regulatory aspects will be discussed based on currently available guidance of direct relevance for polymeric micelles as well as related guidance and suggestions for updates will be provided. The chapter will end with a preview of important developments and breakthroughs that can be anticipated in the (nearby) future and prospects for innovative and generic polymeric micelle drug products.