Modern analytics for synthetically derived complex drug substances: NMR, AFFF–MALS, and MS tests for glatiramer acetate

Integrating Quantitative Methods & Modeling and Analytical Techniques in Reverse Engineering; A Cutting-Edge Strategy in Complex Generic Development

Generic drugs are crucial for healthcare, offering affordable alternatives to brand-name drugs. Complex generics, with intricate ingredients, are gaining increasing importance in managing chronic conditions. However, prior to the regulatory market approval, they must demonstrate similarity in active ingredients, formulations, strength, and administration routes to ensure bioequivalence. The primary constraint lies in demonstrating bioequivalence with the innovator drug using traditional methods includes a lack of advanced technologies, and standardized protocols for analysing complex products. Given the multifaceted nature of these products, a single methodology may not suffice to establish in vitro/in vivo bioequivalence. Recognizing this, the USFDA conducts several workshops aiming advancement of complex generic drug product development. Notably, these efforts highlight the need to use Quantitative Methods and Modeling (QMM) approaches to support generic product development. QMM is a scientific approach used to analyze data and simulate drug development processes, ensuring safe, effective, and similar formulations of generic drugs using mathematical, statistical, and computational tools. QMM facilitates the design of formulations and processes, establishes a framework for in vivo BE studies, and suggests alternative ways to demonstrate BE. Appropriate utilization of the QMM approach can reduce the need for unwanted in vivo studies and bolster in vitro approaches for generic product development. Furthermore, use of orthogonal analytical techniques to characterize and decode innovator drugs can provide valuable insights into product attributes. Integrating this data into QMM enables the assessment of critical material attributes, or critical process parameters, thus demonstrating sameness. The combined application of QMM and analytical techniques not only supports regulatory decisions but also enhances the success rate of complex generic drug products.

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Development of Quantitative Comparative Approaches to Support Complex Generic Drug Development

On October 27-28, 2022, the US Food and Drug Administration (FDA) and the Center for Research on Complex Generics (CRCG) hosted a virtual public workshop titled "Best Practices for Utilizing Modeling Approaches to Support Generic Product Development." This report summarizes the presentations and panel discussions for a session titled "Development of Quantitative Comparative Approaches to Support Complex Generic Drug Development." This session featured speakers and panelists from both the generic industry and the FDA who described applications of advanced quantitative approaches for generic drug development and regulatory assessment within three main topics of interest: (1) API sameness assessment for complex generics, (2) particle size distribution assessment, and (3) dissolution profile similarity comparison. The key takeaways were that the analysis of complex data poses significant challenges to the application of conventional statistical bioequivalence methods, and there are various opportunities for using data analytics approaches for developing and applying suitable equivalence assessment method.

A Precise qNMR Method for the Rapid Quantification of Lot-to-Lot Variations in Multiple Quality Attributes of Pentosan Polysulfate Sodium

Pentosan polysulfate sodium (PPS) is an orphan drug with anticoagulant activity. PPS is prepared from the chemical processing of xylan extracted from beechwood tree to yield a mixture of 4-6 kDa polysaccharides. The chain is mainly composed of sulfated xylose (Xyl) with branched 4-O-methyl-glucuronate (MGA). During generic drug development, the quality attributes (QAs) including monosaccharide composition, modification, and length need to be comparable to those found in the reference list drug (RLD). However, the range of QA variation of the RLD PPS has not been well characterized. Here, multiple PPS RLD lots were studied using quantitative NMR (qNMR) and diffusion ordered spectroscopy (DOSY) to quantitate the components in the mixture and to probe both inter- and intra-lot precision variability. The DOSY precision assessed using coefficient of variation (CV) was 6%, comparable to PPS inter-lot CV of 5%. The QAs obtained from 1D qNMR were highly precise with a precision CV < 1%. The inter-lot MGA content was 4.8 ± 0.1%, indicating a very consistent botanical raw material source. Other process-related chemical modification including aldehyde at 0.51 ± 0.04%, acetylation at 3.3 ± 0.2% and pyridine at 2.08 ± 0.06%, varied more than MGA content. The study demonstrated that 1D qNMR is a quick and precise method to reveal ranges of variation in multiple attributes of RLD PPS which can be used to assess equivalency with generic formulations. Interestingly, the synthetic process appeared to introduce more variations to the PPS product than the botanical source of the material.

Application of 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate derivatization for enhanced peptide mapping analysis of non-biological complex drug glatiramer acetate using liquid chromatography/electrospray ionization collision-induced dissociation high-resolution mass spectrometry

RATIONALE

Glatiramer acetate (GA) (Copaxone®) is a non-biological complex drug (NBCD) comprising random-sequence polymer chains of four amino acids (MW ~ 5-9 kDa) with unknown structure. The characterization of NBCDs by reversed-phase liquid chromatography/mass spectrometry (RPLC/MS) peptide mapping is often impeded by insufficient separation and/or low sensitivity. To overcome this issue, pre-column derivatization of GA peptide digest was used to improve RPLC/MS detectability and to generate a comprehensive peptide profile.

METHODS

Amino groups of peptides generated by trypsin digestion of GA were derivatized using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) reagent. The derivatized mixture of random-sequence peptides was analyzed by liquid chromatography/positive-mode electrospray ionization collision-induced dissociation high-resolution mass spectrometry (RPLC/ESI-CID-HRMS/MS). Data-independent LC/MS^E mode was used for data acquisition.

RESULTS

The derivatization of the GA peptide mixture increased the detectability of RPLC/ESI-CID-HRMS/MS analysis. The efficacy of the procedure was demonstrated by using selected peptides related to different polymeric chain origins. The resultant peptides were derivatized in a predictable manner giving a minimum of side products. The reproducibility of the developed method was demonstrated by comparing peptide elution profiles derived from six Copaxone® lots.

CONCLUSIONS

Application of the AQC pre-column derivatization provides a framework that could be used as an attractive approach for monitoring the quality and characterization of NBCD products in the pharmaceutical industry.

Assessing the Similarity between Random Copolymer Drug Glatiramer Acetate by Using LC-MS Data Coupling with Hypothesis Testing

The full characterization of nonbiological complex drugs (NBCDs) is not possible, but analytical approaches are of urgent need to evaluate the similarity between different lots and compare with their follow-up versions. Here, we propose a hypothesis testing-based approach to assess the similarity/difference between random amino acid copolymer drugs using liquid chromatography mass spectrometry (LC-MS) analysis. Two glatiramer acetate (GA) drugs, commercially available Copaxone and in-house synthesized SPT, and a negative control were digested by Lys-C and followed by HILIC-MS analysis. After retention time alignment and feature identification, 1627 features matched to m/z values in an elemental composition database were considered as derived from active drug ingredients. A hypothesis testing approach, the sum of squared deviations test, was developed to process high-dimensional data derived from LC-MS spectra. The feasibility of this approach was first demonstrated by testing 5 versus 5 lots of Copaxone and Copaxone versus SPT, which suggested a significant similarity by obtaining the estimated 95th percentile of the distribution of the estimator (ρ̂_(95%)) at 0.0056 (p-value = 0.0026) and 0.0026 (p-value < 0.0001), respectively. In contrast, the ρ̂ was 0.036 (p-value = 1.00), while comparing Copaxone and the negative control, implying a lack of similarity. We further synthesized nine stable isotope-labeled peptides to validate the proposed amino acid sequences in the database, demonstrating the correctness in sequence identification. The quantitation variations in our analytical procedures were determined to be 6.8-7.7%. This approach was found to have a great potential for evaluating the similarity between generic NBCDs and listed reference drugs, as well as to monitor the lot-to-lot variation.

Multivariate analysis applied to complex biological medicines

A biological medicine (or biologicals) is a term for a medicinal compound that is derived from a living organism. By their very nature, they are complex and often heterogeneous in structure, composition and biological activity. Some of the oldest pharmaceutical products are biologicals, for example insulin and heparin. The former is now produced recombinantly, with technology being at a point where this can be considered a defined chemical entity. This is not the case for the latter, however. Heparin is a heterogeneous polysaccharide that is extracted from the intestinal mucosa of animals, primarily porcine, although there is also a significant market for non-porcine heparin due to social and economical reasons. In 2008 heparin was adulterated with another sulfated polysaccharide. Unfortunately this event was disastrous and resulted in a global public health emergency. This was the impetuous to apply modern analytical techniques, principally NMR spectroscopy, and multivariate analyses to monitor heparin. Initially, traditional unsupervised multivariate analysis (principal component analysis (PCA)) was applied to the problem. This was able to distinguish animal heparins from each other, and could also separate adulterated heparin from what was considered bona fide heparin. Taught multivariate analysis functions by training the analysis to look for specific patterns within the dataset of interest. If this approach was to be applied to heparin, or any other biological medicine, it would have to be taught to find every possible alien signal. The opposite approach would be more efficient; defining the complex heterogeneous material by a library of bona fide spectra and then filtering test samples with these spectra to reveal alien features that are not consistent with the reference library. This is the basis of an approach termed spectral filtering, which has been applied to 1D and 2D-NMR spectra, and has been very successful in extracting the spectral features of adulterants in heparin, as well as being able to differentiate supposedly biosimilar products. In essence, the filtered spectrum is determined by subtracting the covariance matrix of the library spectra from the covariance matrix of the library spectra plus the test spectrum. These approaches are universal and could be applied to biological medicines such as vaccine polysaccharides and monoclonal antibodies.

Innovation for Generic Drugs: Science and Research Under the Generic Drug User Fee Amendments of 2012

Regulatory science is science and research intended to improve decision making in a regulatory framework. Improvements in decision making can be in both accuracy (making better decisions) and in efficiency (making faster decisions). Science and research supported by the Generic Drug User Fee Amendments of 2012 (GDUFA) have focused on two innovative methodologies that work together to enable new approaches to development and review of generic drugs: quantitative models and advanced in vitro product characterization. Quantitative models faithfully represent current scientific understanding. They are tools pharmaceutical scientists and clinical pharmacologists use for making better and faster product development decisions. Advances in the in vitro product comparisons provide the measurements of product differences that are the critical input into the models. This paper outlines four areas where science and research funded by GDUFA support synergistic use of models and characterization at critical decision points during generic drug product development and review.

Determination of Peptide Profile Consistency and Safety of Collagen Hydrolysates as Quality Attributes

Collagen hydrolysates are dietary supplements used for nutritional and medical purposes. They are complex mixtures of low-molecular-weight peptides obtained from the enzymatic hydrolysis of collagen, which provide intrinsic batch-to-batch heterogeneity. In consequence, the quality of these products, which is related to the reproducibility of their mass distribution pattern, should be addressed. Here, we propose an analytical approach to determine the peptide pattern as a quality attribute of Colagenart®, a product containing collagen hydrolysate. In addition, we evaluated the safety by measuring the viability of two cell lines exposed to the product. The consistency of peptide distribution was determined using Size Exclusion Chromatography (SEC), Mass Spectrometry coupled to a reversed phase UPLC system (MS-RP-UPLC), and Shaped-pulse off-resonance water-presaturation proton nuclear magnetic resonance spectrometry [1 Hwater_presat NMR]. The mass distribution pattern determined by SEC was in a range from 1.35 to 17 kDa, and from 2 to 14 kDa by MS-RP-UPLC. [1 Hwater_presat NMR] showed the detailed spin-systems of the collagen hydrolysates components by global assignment of backbone Hα and NH, as well as side-chain proton resonances. Additionally, short-range intraresidue connectivity pathways of identified spin-regions were obtained by a 2D homonuclear shift correlation Shaped-pulse solvent suppression COSY scheme. Safety analysis of Colagenart® was evaluated in CaCo-2 and HepG2 cells at 2.5 and 25 μg/mL and no negative effects were observed. The results demonstrated batch-to-batch reproducibility, which evinces the utility of this approach to establish the consistency of the quality attributes of collagen hydrolysates. PRACTICAL APPLICATION: We propose state-of-the art analytical methodologies (SEC, MS, and NMR) to evaluate peptide profile and composition of collagen hydrolysates as quality attributes. These methodologies are suitable to be implemented for quality control purposes.

A Simple and Noninvasive DOSY NMR Method for Droplet Size Measurement of Intact Oil-In-Water Emulsion Drug Products

In a typical oil-in-water emulsion drug product, oil droplets with varied sizes are dispersed in a water phase and stabilized by surfactant molecules. The size and polydispersity of oil droplets are critical quality attributes of the emulsion drug product that can potentially affect drug bioavailability. More critically, to ensure accuracy in characterization of the finished drug product, analytical methods should introduce minimal physical perturbation (e.g., temperature variation or dilution) before the analysis. The classical methods of dynamic light scattering or electron microscopy can be used but they generally require sample dilution or harsh preparation conditions, respectively. By contrast, the size distribution of emulsion formulations can be assessed with a simple and noninvasive solution nuclear magnetic resonance method, namely, two-dimensional Diffusion Ordered SpectroscopY. The two-dimensional Diffusion Ordered SpectroscopY method probed signal decay of methyl resonances from oil and sorbate molecules and was applied to 3 types of U.S.-marketed emulsion drug products, that is, difluprednate, cyclosporine, and propofol, yielding measured droplet sizes of 40-280 nm in diameter. The high precision of ±6 nm of the new nuclear magnetic resonance method allows analytical differentiation of lot-to-lot and brand-to-brand droplet size differences in emulsion drug products, critical for drug-quality development, control, and surveillance.

Chemometric Methods to Quantify 1D and 2D NMR Spectral Differences Among Similar Protein Therapeutics

NMR spectroscopy is an emerging analytical tool for measuring complex drug product qualities, e.g., protein higher order structure (HOS) or heparin chemical composition. Most drug NMR spectra have been visually analyzed; however, NMR spectra are inherently quantitative and multivariate and thus suitable for chemometric analysis. Therefore, quantitative measurements derived from chemometric comparisons between spectra could be a key step in establishing acceptance criteria for a new generic drug or a new batch after manufacture change. To measure the capability of chemometric methods to differentiate comparator NMR spectra, we calculated inter-spectra difference metrics on 1D/2D spectra of two insulin drugs, Humulin R® and Novolin R®, from different manufacturers. Both insulin drugs have an identical drug substance but differ in formulation. Chemometric methods (i.e., principal component analysis (PCA), 3-way Tucker3 or graph invariant (GI)) were performed to calculate Mahalanobis distance (D _M) between the two brands (inter-brand) and distance ratio (D _R) among the different lots (intra-brand). The PCA on 1D inter-brand spectral comparison yielded a D _M value of 213. In comparing 2D spectra, the Tucker3 analysis yielded the highest differentiability value (D _M = 305) in the comparisons made followed by PCA (D _M = 255) then the GI method (D _M = 40). In conclusion, drug quality comparisons among different lots might benefit from PCA on 1D spectra for rapidly comparing many samples, while higher resolution but more time-consuming 2D-NMR-data-based comparisons using Tucker3 analysis or PCA provide a greater level of assurance for drug structural similarity evaluation between drug brands.

The random co-polymer glatiramer acetate rapidly kills primary human leukocytes through sialic-acid-dependent cell membrane damage

The formulation glatiramer acetate (GA) is widely used in therapy of multiple sclerosis. GA consists of random copolymers of four amino acids, in ratios that produce a predominantly positive charge and an amphipathic character. With the extraordinary complexity of the drug, several pharmacological modes-of-action were suggested, but so far none, which rationalizes the cationicity and amphipathicity as part of the mode-of-action. Here, we report that GA rapidly kills primary human T lymphocytes and, less actively, monocytes. LL-37 is a cleavage product of human cathelicidin with important roles in innate immunity. It shares the positive charge and amphipathic character of GA, and, as shown here, also the ability to kill human leukocyte. The cytotoxicity of both compounds depends on sialic acid in the cell membrane. The killing was associated with the generation of CD45+ debris, derived from cell membrane deformation. Nanoparticle tracking analysis confirmed the formation of such debris, even at low GA concentrations. Electric cell-substrate impedance sensing measurements also recorded stable alterations in T lymphocytes following such treatment. LL-37 forms oligomers through weak hydrophobic contacts, which is critical for the lytic properties. In our study, SAXS showed that GA also forms this type of contacts. Taken together, our study offers new insight on the immunomodulatory mode-of-action of positively charged co-polymers. The comparison of LL-37 and GA highlights a consistent requirement of certain oligomeric and chemical properties to support cytotoxic effects of cationic polymers targeting human leukocytes.

Advancing pharmaceutical quality: An overview of science and research in the U.S. FDA's Office of Pharmaceutical Quality

Failures surrounding pharmaceutical quality, particularly with respect to product manufacturing issues and facility remediation, account for the majority of drug shortages and product recalls in the United States. Major scientific advancements pressure established regulatory paradigms, especially in the areas of biosimilars, precision medicine, combination products, emerging manufacturing technologies, and the use of real-world data. Pharmaceutical manufacturing is increasingly globalized, prompting the need for more efficient surveillance systems for monitoring product quality. Furthermore, increasing scrutiny and accelerated approval pathways provide a driving force to be even more efficient with limited regulatory resources. To address these regulatory challenges, the Office of Pharmaceutical Quality (OPQ) in the Center for Drug Evaluation and Research (CDER) at the U.S. Food and Drug Administration (FDA) harbors a rigorous science and research program in core areas that support drug quality review, inspection, surveillance, standards, and policy development. Science and research is the foundation of risk-based quality assessment of new drugs, generic drugs, over-the-counter drugs, and biotechnology products including biosimilars. This is an overview of the science and research activities in OPQ that support the mission of ensuring that safe, effective, and high-quality drugs are available to the American public.

Design of a strong cation exchange methodology for the evaluation of charge heterogeneity in glatiramer acetate

Complex pharmaceuticals are in demand of competent analytical methods able to analyze charge heterogeneity as a critical quality attribute (CQA), in compliance with current regulatory expectations. A notorious example is glatiramer acetate (GA), a complex polypeptide mixture useful for the treatment of relapsing-remitting multiple sclerosis. This pharmaceutical challenges the current state of analytical technology in terms of the capacity to study their constituent species. Thus, a strong cation exchange methodology was designed under the lifecycle approach to support the establishment of GA identity, trough the evaluation of its chromatographic profile, which acts as a charge heterogeneity fingerprint. In this regard, a maximum relative margin of error of 5% for relative retention time and symmetry factor were proposed for the analytical target profile. The methodology met the proposed requirements after precision and specificity tests results, the former comprised of sensitivity and selectivity. Subsequently, method validation was conducted and showed that the method is able to differentiate between intact GA and heterogeneity profiles coming from stressed, fractioned or process-modified samples. In summary, these results provide evidence that the method is adequate to assess charge heterogeneity as a CQA of this complex pharmaceutical.