The Application of the Accelerated Stability Assessment Program (ASAP) to Quality by Design (QbD) for Drug Product Stability

An isoconversion paradigm, where times in different temperature and humidity-controlled stability chambers are set to provide a fixed degradant level, is shown to compensate for the complex, non-single order kinetics of solid drug products. A humidity-corrected Arrhenius equation provides reliable estimates for temperature and relative humidity effects on degradation rates. A statistical protocol is employed to determine best fits for chemical stability data, which in turn allows for accurate estimations of shelf life (with appropriate confidence intervals) at any storage condition including inside packaging (based on the moisture vapor transmission rate of the packaging and moisture sorption isotherms of the internal components). These methodologies provide both faster results and far better predictions of chemical stability limited shelf life (expiry) than previously possible. Precise shelf-life estimations are generally determined using a 2-week, product-specific protocol. Once the model for a product is developed, it can play a critical role in providing the product understanding necessary for a quality by design (QbD) filing for product approval and enable rational control strategies to assure product stability. Moreover, this Accelerated Stability Assessment Program (ASAP) enables the coupling of product attributes (e.g., moisture content, packaging options) to allow for flexibility in how control strategies are implemented to provide a balance of cost, speed, and other factors while maintaining adequate stability.     

Transport and kinetic processes underlying biomolecular interactions in the BIACORE optical biosensor

The transport and kinetic processes describing biomolecular interactions in the BIACORE optical biosensor have been studied with the help of a mathematical model. In comparison to previous models, the model presented here couples, for the first time, transport phenomena in the flow channel with hindered diffusive transport and reactions inside the hydrogel. Simulated experiments based on this model, and two simpler models extant in the literature, are used to identify cases under which the detailed model is essential for accurate prediction of kinetic parameters. It is shown that this model can substantially improve the accuracy of kinetic parameter estimation when transport limitations in the flow channel and/or the hydrogel significantly influence the observed instrument response curves. The model can extend the range of the instrument's applicability to higher concentrations of immobilized species within the hydrogel. It can also be used for accurate design of experiments with the purpose of minimizing errors in the estimation of the kinetic parameters.     

Model-based monitoring of industrial reversed phase chromatography to predict insulin variants

Downstream processing in the manufacturing biopharmaceutical industry is a multistep process separating the desired product from process- and product-related impurities. However, removing product-related impurities, such as product variants, without compromising the product yield or prolonging the process time due to extensive quality control analytics, remains a major challenge. Here, we show how mechanistic model-based monitoring, based on analytical quality control data, can predict product variants by modeling their chromatographic separation during product polishing with reversed phase chromatography. The system was described by a kinetic dispersive model with a modified Langmuir isotherm. Solely quality control analytical data on product and product variant concentrations were used to calibrate the model. This model-based monitoring approach was developed for an insulin purification process. Industrial materials were used in the separation of insulin and two insulin variants, one eluting at the product peak front and one eluting at the product peak tail. The model, fitted to analytical data, used one component to simulate each protein, or two components when a peak displayed a shoulder. This monitoring approach allowed the prediction of the elution patterns of insulin and both insulin variants. The results indicate the potential of using model-based monitoring in downstream polishing at industrial scale to take pooling decisions.     

Reactive Impurities in Excipients: Profiling,Identification and Mitigation of Drug–Excipient Incompatibility

Reactive impurities in pharmaceutical excipients could cause drug product instability, leading to decreased product performance, loss in potency, and/or formation of potentially toxic degradants. The levels of reactive impurities in excipients may vary between lots and vendors. Screening of excipients for these impurities and a thorough understanding of their potential interaction with drug candidates during early formulation development ensure robust drug product development. In this review paper, excipient impurities are categorized into six major classes, including reducing sugars, aldehydes, peroxides, metals, nitrate/nitrite, and organic acids. The sources of generation, the analytical method for detection, the stability of impurities upon storage and processing, and the potential reactions with drug candidates of these impurities are reviewed. Specific examples of drug–excipient impurity interaction from internal research and literature are provided. Mitigation strategies and corrective measures are also discussed.     

Statistical modeling of cell-to-cell variability in viral infection during passaging in suspension cell culture: Application in Monte-Carlo simulation

Packaging during the passaging of viruses in cell cultures yields various phenotypes and is regulated by viral protein expression in infected cells. Although such a packaging mechanism has a profound effect in controlling the virus yield, little is known about the underlying statistical models followed by virus packaging and protein expression among cells infected with the virus. A predictive framework combining identification of the probability density function (PDF) based on log-likelihood and using the PDF for Monte-Carlo simulations is developed. The Birnbaum–Saunders distribution was found to be consistent with all three-virus packaging levels, including nucleocapsids/occlusion-derived virus (ODV), ODVs/polyhedra, and polyhedra/cell for both wild-type and genetically modified AcMNPV. Next, it was demonstrated that PDF fitting could be used to compare two viruses having distinctly different genetic configurations. Finally, the identified PDF can be incorporated in RNA synthesis parameters for baculovirus infection to predict the cell-to-cell variability in protein expression using Monte-Carlo simulations. The proposed tool can be used for the estimation of uncertainty in the kinetic parameter and prediction of cell-to-cell variability for other biological systems.     

Modelling atypical CYP3A4 kinetics: principles and pragmatism

The Michaelis-Menten model, and the existence of a single active site for the interaction of substrate with drug metabolizing enzyme, adequately describes a substantial number of in vitro metabolite kinetic data sets for both clearance and inhibition determination. However, in an increasing number of cases (involving most notably, but not exclusively, CYP3A4), atypical kinetic features are observed, e.g., auto- and heteroactivation; partial, cooperative, and substrate inhibition; concentration-dependent effector responses (activation/inhibition); limited substrate substitution and inhibitory reciprocity necessitating sub-group classification. The phenomena listed above cannot be readily interpreted using single active site models and the literature indicates that three types of approaches have been adopted. First the 'nai ve' approach of using the Michaelis-Menten model regardless of the kinetic behaviour, second the 'empirical' approach (e.g., employing the Hill or uncompetitive inhibition equations to model homotropic phenomena of sigmoidicity and substrate inhibition, respectively) and finally, the 'mechanistic' approach. The later includes multisite kinetic models derived using the same rapid equilibrium/steady-state assumptions as the single-site model. These models indicate that 2 or 3 binding sites exist for a given CYP3A4 substrate and/or effector. Multisite kinetic models share common features, depending on the substrate kinetics and the nature of the effector response observed in vitro, which allow a generic model to be proposed. Thus although more complex than the other two approaches, they show more utility and can be comprehensively applied in relatively simple versions that can be readily generated from generic model. Multisite kinetic features, observed in isolated hepatocytes as well as in microsomes from hepatic tissue and heterologous expression systems, may be evident in substrate depletion-time profiles as well as in metabolite formation rates. Failure to adequately account for multisite kinetic phenomena will compromise any attempts to predict human drug clearance and drug-drug interaction potential from in vitro data.     

Profiling degradants of paclitaxel using liquid chromatography–mass spectrometry and liquid chromatography–tandem mass spectrometry substructural techniques

A rapid and systematic strategy based on liquid chromatography–mass spectrometry (LC–MS) profiling and liquid chromatography–tandem mass spectrometry (LC–MS–MS) substructural techniques was utilized to elucidate the degradation products of paclitaxel, the active ingredient in Taxol. This strategy integrates, in a single instrumental approach, analytical HPLC, UV detection, full-scan electrospray MS, and MS–MS to rapidly and accurately elucidate structures of impurities and degradants. In these studies, degradants induced by acid, base, peroxide, and light were profiled using LC–MS and LC–MS–MS methodologies resulting in an LC–MS degradant database which includes information on molecular structures, chromatographic behavior, molecular mass, and MS–MS substructural information. The stressing conditions which may cause drug degradation are utilized to validate the analytical monitoring methods and serve as predictive tools for future formulation and packaging studies. Degradation products formed upon exposure to basic conditions included baccatin III, paclitaxel sidechain methyl ester, 10-deacetylpaclitaxel, and 7-epipaclitaxel. Degradation products formed upon exposure to acidic conditions included 10-deacetylpaclitaxel and the oxetane ring opened product. Treatment with hydrogen peroxide produced only 10-deacetylpaclitaxel. Exposure to high intensity light produced a number of degradants. The most abundant photodegradant of paclitaxel corresponded to an isomer which contains a C3–C11 bridge. These methodologies are applicable at any stage of the drug product cycle from discovery through development. This library of paclitaxel degradants provides a foundation for future development work regarding product monitoring, as well as use as a diagnostic tool for new degradation products.     

Studies of folding and misfolding using simplified models

Computer simulations are as vital to our studies of biological systems as experiments. They bridge and rationalize experimental observations, extend the experimental "field of view", which is often limited to a specific time or length scale, and, most importantly, provide novel insights into biological systems, offering hypotheses about yet-to-be uncovered phenomena. These hypotheses spur further experimental discoveries. Simplified molecular models have a special place in the field of computational biology. Branded as less accurate than all-atom protein models, they have offered what all-atom molecular dynamics simulations could not--the resolution of the length and time scales of biological phenomena. Not only have simplified models proven to be accurate in explaining or reproducing several biological phenomena, they have also offered a novel multiscale computational strategy for accessing a broad range of time and length scales upon integration with traditional all-atom simulations. Recent computer simulations of simplified models have shaken or advanced the established understanding of biological phenomena. It was demonstrated that simplified models can be as accurate as traditional molecular dynamics approaches in identifying native conformations of proteins. Their application to protein structure prediction yielded phenomenal accuracy in recapitulating native protein conformations. New studies that utilize the synergy of simplified protein models with all-atom models and experiments yielded novel insights into complex biological processes, such as protein folding, aggregation and the formation of large protein complexes.     

植物气孔气态失水与SPAC系统液态供水的相互调节作用研究进展

讨论了植物气孔气态失水与SPAC系统液态供水相互作用研究领域的一些重要现象和行为.当植物水力信号和化学信号共同作用促进气孔对叶水势的调节时,植物对叶水势的调节表现为等水行为.气孔对环境湿度变化响应的反馈机制可用来解释土壤干旱条件下气孔和光合的午休现象,以及气孔导度和水流导度之间的相关关系;而气孔对环境湿度变化响应的前馈机制,则可用来解释气孔导度对大气 叶片间水汽饱和差的滞后反应.植物最大限度地利用木质部传输水分的策略,要求气孔快速响应以避免木质部过度气穴化和短时间内将气穴逆转的相应机制.     

An adaptive soft-sensor for advanced real-time monitoring of an antibody-drug conjugation reaction

In the production of antibody-drug conjugates (ADCs), the conjugation reaction is a central step defining the final product composition and, hence, directly affecting product safety and efficacy. To enable real-time monitoring, spectroscopic sensors in combination with multivariate regression models have gained popularity in recent years. The extended Kalman filter (EKF) can be used as so-called soft-sensor to fuse sensor predictions with long-horizon forecasts by process models. This enables the dynamic update of the current state and provides increased robustness against experimental noise or model errors. Due to the uncertainty associated with sensor and process models in biopharmaceutical applications, the deployment of such soft-sensors is challenging. In this study, we demonstrate the combination of an uncertainty-aware sensor model with a kinetic reaction model using an EKF to monitor a site-directed ADC conjugation reaction. As the sensor model, a Gaussian process regression model is presented to realize a time-variant determination of the sensor uncertainty. The EKF fuses the time-discrete predictions of the amount of conjugated drug from the sensor model with the time-continuous predictions from the kinetic model. While the ADC species are not distinguishable by on-line recorded UV/Vis spectra, the developed soft-sensor is able to dynamically update all relevant reaction species. It could be shown that the use of time-variant process and sensor noise computation approaches improved the performance of the EKF and achieved a reduction of the prediction error of up to 23% compared with the kinetic model. The developed framework proved to enhance robustness against noisy sensor measurements or wrong model initialization and was successfully transferred from batch to fed-batch mode. In future, this framework could be implemented for model-based process control and be adopted for other ADC conjugation reaction types.     

Kinetic analysis of drug-induced G2 block in vitro

The data on cell-cycle effects of two prospective antitumour agents, (+)-1,2,-bis(3,5-dioxopiperazine-1-yl)propane (soluble ICRF; NSC 169780) and 1,4-bis(2'chloroethyl)-1,4-diazabicyclo [2.2.1] heptane diperchlorate (CBH; NSC 57198) were used to determine whether a modified stathmokinetic experiment could predict the effects of continuous, long-term (0-48 hr) drug exposure in an in vitro L1210 murine leukaemia cell system. Generally, continuous drug exposure of exponentially growing cells does not provide sufficient quantitative information concerning cell-cycle-phase-specific mechanisms of drug action. Alternatively, stathmokinetic experiments, which are usually limited to some fraction of one cell doubling time, provide little information about long-term drug effects. By using mathematical models constructed for this purpose, however, stathmokinetic data can predict the overall proportion of cells affected by a drug though failing to discern between various kinds of drug action (e.g. reversible v. irreversible block, blocking v. killing action, etc.), especially when it occurs in G2 phase. In addition, it can be shown that for at least one of the drugs (soluble ICRF) the stathmokinetic experiment fails to predict 'after-effects' of drug treatment which extend into the following cell cycle(s). It also becomes clear that the degradation of exponential growth characteristics of quickly dividing cells during long-term, continuous drug exposure makes prediction of cell-cycle kinetic perturbations uncertain when derived from short-duration stathmokinetic experiments. However, with care, the joint application of 'short term' (e.g. stathmokinesis) and 'long term' (e.g. continuous exposure) techniques allow adequate quantitative insight into drug-perturbed cell-cycle kinetics. The applicability of modelling techniques is discussed: in the present instance it is limited to lower drug concentrations. For higher drug concentrations, effects like increased ploidy, ineffective division, etc., make it impossible in the present study to obtain a clear picture of the kinetics.     

Novel mathematical method for quantitative expression of deviation from the Higuchi model

A simple mathematical method to express the deviation in release profile of a test product following Higuchi's kinetics from an ideal Higuchi release profile was developed. The method is based on calculation of area under the curve (AUC) by using the trapezoidal rule. The precision of prediction depends on the number of data points. The method is exemplified for 2 dosage forms (tablets of diltiazem HCl and microspheres of diclofenac sodium) that are designed to release the drug over a 12-hour period. The method can be adopted for the formulations where drug release is incomplete (<100%) or complete (100%) at last sampling time. To describe the kinetics of drug release from the test formulation, zero-order, first-order, Higuchi's. Hixson-Crowell's, and Weibull's models were used. The criterion for selecting the most appropriate model was based on the goodness-of-fit test. The release kinetics of the tablets and microspheres were explained by the Higuchi model. The release profiles of the test batches were slightly below the ideal Higuchi release profile. For the test products, observed percentage deviation from an ideal Higuchi profile is less than 16% for tablets and less than 11% for microspheres. The proposed method can be extended to the modified release formulations that are designed to release a drug over 6, 18, or 24 hours. If the data points are not evenly separated, the ideal drug release profile and AUC are calculated according to the specific sampling time. The proposed method may be used for comparing formulated products during the research and development stage, for quality control of the products, or for promoting products by comparing performance of the test product with that of the innovator's product.     

Controlled Extraction Studies Applied to Polyvinyl Chloride and Polyethylene Materials: Conclusions from the ELSIE Controlled Extraction Pilot Study

The effective management of leachables in pharmaceutical products is a critical aspect of their development. This can be facilitated if extractables information on the materials used in a packaging or delivery system is available to assist companies in selecting materials that will be compatible with the drug product formulation and suitable for the intended use. The Extractables and Leachables Safety Information Exchange (ELSIE) materials working group developed and executed a comprehensive extraction study protocol that included a number of extraction solvents, extraction techniques, and a variety of analytical techniques. This was performed on two test materials, polyethylene (PE) and polyvinyl chloride (PVC), that were selected due to their common use in pharmaceutical packaging. The purpose of the study was to investigate if the protocol could be simplified such that (i) a reduced number or even a single extraction technique could be used and (ii) a reduced number of solvents could be used to obtain information that is useful for material selection regardless of product type. Results indicate that, at least for the PVC, such reductions are feasible. Additionally, the studies indicate that levels of extractable elemental impurities in the two test materials were low and further confirm the importance of using orthogonal analytical detection techniques to gain adequate understanding of extraction profiles.KEY WORDS: extractables, extraction technique, polyethylene, polyvinyl chloride     

A mechanistic kinetic description of lactate dehydrogenase elucidating cancer diagnosis and inhibitor evaluation

As a key enzyme for glycolysis, lactate dehydrogenase (LDH) remains as a topic of great interest in cancer study. Though a number of kinetic models have been applied to describe the dynamic behavior of LDH, few can reflect its actual mechanism, making it difficult to explain the observed substrate and competitor inhibitions at wide concentration ranges. A novel mechanistic kinetic model is developed based on the enzymatic processes and the interactive properties of LDH. Better kinetic simulation as well as new enzyme interactivity information and kinetic properties extracted from published articles via the novel model was presented. Case studies were presented to a comprehensive understanding of the effect of temperature, substrate, and inhibitor on LDH kinetic activities for promising application in cancer diagnosis, inhibitor evaluation, and adequate drug dosage prediction.     

A calculator program for clinical application of the Bayesian method of predicting plasma drug levels

A pharmacokinetic program that allows individualization of drug dosage regimens through the Bayesian method is described. The program, which is designed for the Hewlett-Packard HP-41 CV calculator, is based upon the one-compartment open model with either instantaneous or zero-order absorption. Individualized estimation of the patient's kinetic parameters (clearance and volume of distribution) is performed by analyzing the plasma levels measured in the patient as well as considering the population data of the drug. After estimating the individual kinetic parameters by the Bayesian method, the program predicts the dosage regimen that will elicit the desired peak and trough plasma levels at steady state. For comparison purposes, the least-squares estimates for clearance and volume of distribution are calculated, and dosage prediction can also be made on the basis of the least-squares estimates. The least-squares estimates can be used to calculate population pharmacokinetic parameters according to the Standard Two-Stage method. Several examples of clinical use of the program are presented. The examples refer to patients with classic hemophilia who were treated with Factor VIII concentrates. In these patients, the Bayesian kinetic parameters of Factor VIII have been estimated through the calculator program. The Bayesian parameter estimates generated by the HP-41 have been compared with those determined by a Bayesian program (ADVISE) designed for microcomputers.     

Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.     

Mathematical modeling of an aqueous film coating process in a Bohle Lab-Coater: Part 2: Application of the model

For the prediction of the air and product temperatures, the product moisture, and the air humidity during a coating process in a Bohle Lab-Coater, a model was developed. The purpose of this work was to determine the limit moisture, the critical moisture, and the constant for the exchange rate between both zones and to use these values for other sets of experiments to test the model. The adaptation of the 3 parameters (limit moisture, critical moisture, and exchange rate constant), was done by calculation of the product temperature in both zones for several sets of parameters in order to minimize the sum of square deviation between the calculated and the measured product temperatures. This set of parameters was used to test the validity of the model. By applying the model, the product temperature could be predicted based on the product, process, and equipment-related parameters. Hence, the model can be used to theoretically investigate the influence of different process paramaters. The mean difference between the predicted, and measured product temperatures in the steady state is ≈2 up to 3 K using the determined parameter set for the limit moisture, the critical moisture, and the exchange rate constant. The model is useful for the prediction of the air and product temperatures, the product moisture, and air humidity during a coating process in the Bohle Lab-Coater using round, biconvex tablets.     

Kinetic Analysis of Drug-Induced G2 Block In Vitro

The data on cell-cycle effects of two prospective antitumour agents, (+)-1,2,-bis(3,5-dioxopiperazine-l-yl)propane (soluble ICRF; NSC 169780) and 1,4-bis(2′chloroethyl)-1,4-diazabicyclo [2.2.1] heptane diperchlorate (CBH; NSC 57198) were used to determine whether a modified stathmokinetic experiment could predict the effects of continuous, long-term (0–48 hr) drug exposure in an in vitro L1210 murine leukaemia cell system. Generally, continuous drug exposure of exponentially growing cells does not provide sufficient quantitative information concerning cell-cycle-phase-specific mechanisms of drug action. Alternatively, stathmokinetic experiments, which are usually limited to some fraction of one cell doubling time, provide little information about long-term drug effects. By using mathematical models constructed for this purpose, however, stathmokinetic data can predict the overall proportion of cells affected by a drug though failing to discern between various kinds of drug action (e.g. reversible v. irreversible block, blocking v. killing action, etc.), especially when it occurs in G₂ phase. In addition, it can be shown that for at least one of the drugs (soluble ICRF) the stathmokinetic experiment fails to predict ‘after-effects’ of drug treatment which extend into the following cell cycle(s). It also becomes clear that the degradation of exponential growth characteristics of quickly dividing cells during long-term, continuous drug exposure makes prediction of cell-cycle kinetic perturbations uncertain when derived from short-duration stathmokinetic experiments. However, with care, the joint application of ‘short term’ (e.g. stathmokinesis) and ‘long term’ (e.g. continuous exposure) techniques allow adequate quantitative insight into drug-perturbed cell-cycle kinetics. the applicability of modelling techniques is discussed: in the present instance it is limited to lower drug concentrations. For higher drug concentrations, effects like increased ploidy, ineffective division, etc., make it impossible in the present study to obtain a clear picture of the kinetics.     

基于冠层温湿度模型的日光温室黄瓜霜霉病预警方法

利用温室环境参数构建室内微环境模拟模型,并结合温室病害模型进行预警,便于开展病害生态防治,以减少农药使用,从而保护温室生态环境和保证农产品质量安全.本文利用温室内能量守恒原理和水分平衡原理,构建了日光温室冠层叶片温度和空气相对湿度模拟模型.叶片温度模拟模型考虑了温室内植物与墙体、土壤、覆盖物之间的辐射热交换,以及室内净辐射、叶片蒸腾作用引起的能量变化;相对湿度模拟模型综合了温室内叶片蒸腾、土壤蒸发、覆盖物与叶面的水汽凝结引起的水分变化.将温湿度估计模型输出值作为参数,输入黄瓜霜霉病初侵染和潜育期预警模型中,估计黄瓜霜霉病发病日期,并与田间观测的实际发病日期比较.试验选取2014年9月和10月的温湿度监测数据进行模型验证,冠层叶片温度实际值与模拟值的均方根偏差（RMSD)分别为0.016和0.024 ℃,空气相对湿度实际值与模拟值的RMSD分别为0.15%和0.13%.结合温湿度估计模型结果表明,黄瓜病害预警系统预测黄瓜霜霉病发病日期与田间调查发病日期相吻合.本研究可为黄瓜日光温室病害预警模型及系统构建提供微环境数据支持.