Estimation of raw material performance in mammalian cell culture using near infrared spectra combined with chemometrics approaches

Understanding variability in raw materials and their impacts on product quality is of critical importance in the biopharmaceutical manufacturing processes. For this purpose, several spectroscopic techniques have been studied for raw material characterization, providing fast and nondestructive ways to measure quality of raw materials. However, investigations of correlation between spectra of raw materials and cell culture performance have been scarce due to their complexity and uncertainty. In this study, near-infrared spectra and bioassays of multiple soy hydrolysate lots manufactured by different vendors were analyzed using chemometrics approaches in order to address variability of raw materials as well as correlation between raw material properties and corresponding cell culture performance. Principal component analysis revealed that near-infrared spectra of different soy lots contain enough physicochemical information about soy hydrolysates to allow identification of lot-to-lot variability as well as vendor-to-vendor differences. The identified compositional variability was further analyzed in order to estimate cell growth and protein production of two mammalian cell lines under the condition of varying soy dosages using partial least square regression combined with optimal variable selection. The performance of the resulting models demonstrates the potential of near-infrared spectroscopy as a robust lot selection tool for raw materials while providing a biological link between chemical composition of raw materials and cell culture performance.     

Characterization of mammalian cell culture raw materials by combining spectroscopy and chemometrics

Two of the primary issues with characterizing the variability of raw materials used in mammalian cell culture, such as wheat hydrolysate, is that the analyses of these materials can be time consuming, and the results of the analyses are not straightforward to interpret. To solve these issues, spectroscopy can be combined with chemometrics to provide a quick, robust and easy to understand methodology for the characterization of raw materials; which will improve cell culture performance by providing an assessment of the impact that a given raw material will have on final product quality. In this study, four spectroscopic technologies: near infrared spectroscopy, middle infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy were used in conjunction with principal component analysis to characterize the variability of wheat hydrolysates, and to provide evidence that the classification of good and bad lots of raw material is possible. Then, the same spectroscopic platforms are combined with partial least squares regressions to quantitatively predict two cell culture critical quality attributes (CQA): integrated viable cell density and IgG titer. The results showed that near infrared (NIR) spectroscopy and fluorescence spectroscopy are capable of characterizing the wheat hydrolysate's chemical structure, with NIR performing slightly better; and that they can be used to estimate the raw materials’ impact on the CQAs. These results were justified by demonstrating that of all the components present in the wheat hydrolysates, six amino acids: arginine, glycine, phenylalanine, tyrosine, isoleucine and threonine; and five trace elements: copper, phosphorus, molybdenum, arsenic and aluminum, had a large, statistically significant effect on the CQAs, and that NIR and fluorescence spectroscopy performed the best for characterizing the important amino acids. It was also found that the trace elements of interest were not characterized well by any of the spectral technologies used; however, the trace elements were also shown to have a less significant effect on the CQAs than the amino acids. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers, 33:1127–1138, 2017     

Data fusion-based assessment of raw materials in mammalian cell culture

In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non‐destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near‐infrared, Raman, 2D fluorescence, and X‐ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality. Biotechnol. Bioeng. 2012; 109: 2819–2828. © 2012 Wiley Periodicals, Inc.     

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real‐time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration‐related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1586–1599, 2015     

Delayed luminescence: an experimental protocol for Chinese herbal medicines

In Chinese medicine, raw herbal materials are used in processed and unprocessed forms aiming to meet the different requirements of clinical practice. To assure the chemical quality and therapeutic properties of the herbs, fast and integrated systematic assays are required. So far, such assays have not been established. Delayed luminescence (DL) refers to a decaying long‐term ultraweak photon emission after exposure to light. Its decay kinetics under certain conditions may be a sensitive indicator reflecting the internal structural and chemical/physiological state of a biological system. DL measurements have been used in many applications for quality control. However, relatively little research has been reported on dried plant material such as Chinese herbs. The objective of the present study is to establish a protocol for direct and rapid DL measurements of dried Chinese herbal materials, including the determination of the dependence on: (a) the optimal excitation time utilizing a white light source; (b) the optimal size of the grinded herbal particle; and (c) the humidity conditions before and during measurement. Results indicate that stable and reproducible curves of DL photon emission depend mainly on the water content of herbal materials. To investigate the application of the established DL measurement protocol, non‐processed and processed Aconitum (Aconitum carmichaelii Debx.), wild and cultivated rhubarb (Rheum palmatum L.) and ginseng (Panax ginseng C.A.Mey) of different ages were measured using DL. The results suggest that DL technology is a potential tool for assessment of dried Chinese herb qualities. The results warrant a further exploration of this technique in relation to therapeutic properties of the herbs. Copyright © 2016 John Wiley & Sons, Ltd.     

De-risking Pharmaceutical Tablet Manufacture Through Process Understanding,Latent Variable Modeling,and Optimization Technologies

In pharmaceutical tablet manufacturing processes, a major source of disturbance affecting drug product quality is the (lot-to-lot) variability of the incoming raw materials. A novel modeling and process optimization strategy that compensates for raw material variability is presented. The approach involves building partial least squares models that combine raw material attributes and tablet process parameters and relate these to final tablet attributes. The resulting models are used in an optimization framework to then find optimal process parameters which can satisfy all the desired requirements for the final tablet attributes, subject to the incoming raw material lots. In order to de-risk the potential (lot-to-lot) variability of raw materials on the drug product quality, the effect of raw material lot variability on the final tablet attributes was investigated using a raw material database containing a large number of lots. In this way, the raw material variability, optimal process parameter space and tablet attributes are correlated with each other and offer the opportunity of simulating a variety of changes in silico without actually performing experiments. The connectivity obtained between the three sources of variability (materials, parameters, attributes) can be considered a design space consistent with Quality by Design principles, which is defined by the ICH-Q8 guidance (USDA 2006). The effectiveness of the methodologies is illustrated through a common industrial tablet manufacturing case study.     

Protein A chromatography resin lifetime—impact of feed composition

Adsorbent lifetime during protein A chromatography is not readily predicted or understood, representing a key challenge to be addressed for biopharmaceutical manufacturers. This article focuses on the impact of feed composition on the performance of a typical agarose‐based protein A resin across a lifetime of 50 cycles. Cycling studies were performed using three different feed materials with varying levels of feed components including proteases, histones, DNA, and nonhistone proteins. Changes in the process and quality attributes were measured. The DBCs were not seen to vary between conditions although there was a reduction in particle porosity in all cases. Fluorescence spectroscopy and LC‐MS/MS were used to identify the contribution and extent of fouling to the observed capacity loss. Residual protein A ligand density and deposition of foulants (HCP, residual mAb, and DNA) varied between the three feed materials. Resins cycled in feed materials containing high concentrations of HCP and histones were seen to have greater extents of capacity loss. The mode of performance loss, capacity loss, or impact on product quality was seen to vary depending on the feed material. The results indicate that feed material composition may be correlated to the rate and mode of resin aging as a basis for improved process understanding. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:412–419, 2018     

Chemometric Analysis for Identification of Botanical Raw Materials for Pharmaceutical Use: A Case Study Using Panax notoginseng

The overall control of the quality of botanical drugs starts from the botanical raw material, continues through preparation of the botanical drug substance and culminates with the botanical drug product. Chromatographic and spectroscopic fingerprinting has been widely used as a tool for the quality control of herbal/botanical medicines. However, discussions are still on-going on whether a single technique provides adequate information to control the quality of botanical drugs. In this study, high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), capillary electrophoresis (CE) and near infrared spectroscopy (NIR) were used to generate fingerprints of different plant parts of Panax notoginseng. The power of these chromatographic and spectroscopic techniques to evaluate the identity of botanical raw materials were further compared and investigated in light of the capability to distinguishing different parts of Panax notoginseng. Principal component analysis (PCA) and clustering results showed that samples were classified better when UPLC- and HPLC-based fingerprints were employed, which suggested that UPLC- and HPLC-based fingerprinting are superior to CE- and NIR-based fingerprinting. The UPLC- and HPLC- based fingerprinting with PCA were able to correctly distinguish between samples sourced from rhizomes and main root. Using chemometrics and its ability to distinguish between different plant parts could be a powerful tool to help assure the identity and quality of the botanical raw materials and to support the safety and efficacy of the botanical drug products.     

Predicting Mab product yields from cultivation media components, using near-infrared and 2D-fluorescence spectroscopies

The yield of monoclonal antibody (Mab) production processes depends on media formulation, inocula quality, and process conditions. As in industrial processes tight cultivation conditions are used, and inocula quality and viable cell densities are controlled to reasonable levels, media formulation and raw materials lot-to-lot variability in quality will have, in those circumstances, the highest impact on process performance. In the particular Mab process studied, two different raw materials were used: a complex carbon and nitrogen source made of specific peptones and defined chemical media containing multiple components. Using different spectroscopy techniques for each of the raw material types, it was concluded that for the complex peptone-based ingredient, near-infrared (NIR) spectroscopy was more capable of capturing lot-to-lot variability. For the chemically defined media containing fluorophores, two-dimensional (2D)-fluorescence spectroscopy was more capable of capturing lot-to-lot variability. Because in Mab cultivation processes both types of raw materials are used, combining the NIR and 2D-fluorescence spectra for each of the media components enabled predictive models for yield to be developed that out-performed any other model involving either one raw material alone, or only one type of spectroscopic tool for both raw materials. For each particular raw material, the capability of each spectroscopy to detect lot-to-lot differences was demonstrated after spectra preprocessing and specific wavelength regions selection. The work described and the findings reported here open up several possibilities that could be used to feed-forward control the process. These include, for example, enabling specific actions to be taken regarding media formulation with particular lots, and all types of predictive control actions aimed at increasing batch-to-batch yield and product quality consistency at harvest.     

Environmental assessment of thermal insulation composite material

Purpose

This paper presents life cycle assessment of planned mass production of the thermal insulation blocks (TIB) made of thermal insulation composite material (TICM) from secondary raw materials—glass and plastic. This material is being developed at Brno University of Technology, Faculty of Civil Engineering for use in structural details of (especially low energy or passive) buildings subjected to higher compressive loads. Two production modes depending on the quality of the input materials are compared.

Methods

The assessment is conducted using GaBi 4 software tool with inbuilt Ecoinvent database. The results of the assessment are presented in individual impact categories according to used characterization model (CML 2001—Dec. 07). All the necessary energy and material flows are specified in detail for the purpose of the assessment. Cut-off allocation method is used for allocating the environmental impacts of recycled materials. Part of the assessment is sensitivity analysis of one variable parameter—amount of TIB produced per year.

Results and discussion

The results of the assessment show decisive impact of used electricity source on the overall results—86.2 and 94.3 %, respectively, for both production modes. This is closely connected with quality of used secondary raw materials and design of the production line. Use of higher-quality materials, as well as changes of the designed production line can reduce the overall environmental impacts by almost 30 %.

Conclusions

The results show possible improvements in the planned mass production of the TIB. They also find that further investigation will be required before the start of mass production, especially in connection with improving the environmental impacts of used electricity sources.     

Sulfanilic acid‐modified chitosan mini‐spheres and their application for lysozyme purification from egg white

A cation exchange matrix with zwitterionic and multimodal properties was synthesized by a simple reaction sequence coupling sulfanilic acid to a chitosan based support. The novel chromatographic matrix was physico‐chemically characterized by ss‐NMR and ζ potential, and its chromatographic performance was evaluated for lysozyme purification from diluted egg white. The maximum adsorption capacity, calculated according to Langmuir adsorption isotherm, was 50.07 ± 1.47 mg g^?1 while the dissociation constant was 0.074 ± 0.012 mg mL^?1. The process for lysozyme purification from egg white was optimized, with 81.9% yield and a purity degree of 86.5%, according to RP‐HPLC analysis. This work shows novel possible applications of chitosan based materials. The simple synthesis reactions combined with the simple mode of use of the chitosan matrix represents a novel method to purify proteins from raw starting materials. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 34:387–396, 2018     

Strategic deployment of CHO expression platforms to deliver Pfizer's Monoclonal Antibody Portfolio

Development of stable cell lines for expression of large‐molecule therapeutics represents a significant portion of the time and effort required to advance a molecule to enabling regulatory toxicology studies and clinical evaluation. Our development strategy employs two different approaches for cell line development based on the needs of a particular project: a random integration approach for projects where high‐level expression is critical, and a site‐specific integration approach for projects in which speed and reduced employee time spend is a necessity. Here we describe both our random integration and site‐specific integration platforms and their applications in support of monoclonal antibody development and production. We also compare product quality attributes of monoclonal antibodies produced with a nonclonal cell pool or clonal cell lines derived from the two platforms. Our data suggests that material source (pools vs. clones) does not significantly alter the examined product quality attributes. Our current practice is to leverage this observation with our site‐specific integration platform, where material generated from cell pools is used for an early molecular assessment of a given candidate to make informed decisions around development strategy. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1463–1467, 2017     

Development of downstream processing to minimize beta‐glucan impurities in GMP‐manufactured therapeutic antibodies

The presence of impurities or contaminants in biological products such as monoclonal antibodies (mAb) could affect efficacy or cause adverse reactions in patients. ICH guidelines (Q6A and Q6B) are in place to regulate the level of impurities within clinical drug products. An impurity less often reported and, therefore, lacking regulatory guideline is beta‐glucan. Beta‐glucans are polysaccharides of d ‐glucose monomers linked by (1‐3) beta‐glycosidic bonds, and are produced by prokaryotic and eukaryotic organisms, including plants. They may enter manufacturing processes via raw materials such as cellulose‐based membrane filters or sucrose. Here we report the detection of beta‐glucan contamination of a monoclonal IgE antibody (MOv18), manufactured in our facility for a first‐in‐human, first‐in‐class clinical trial in patients with cancer. Since beta‐glucans have potential immunostimulatory properties and can cause symptomatic infusion reactions, it was of paramount importance to identify the source of beta‐glucans in our product and to reduce the levels to clinically insignificant concentrations. We identified beta‐glucans in sucrose within the formulation buffer and within the housing storage buffer of the virus removal filter. We also detected low level beta‐glucan contamination in two of four commercially available antibodies used in oncology. Both formulation buffers contained sucrose. We managed to reduce levels of beta‐glucan in our product 10‐fold, by screening all sucrose raw material, filtering the sucrose by Posidyne® membrane filtration, and by incorporating extra wash steps when preparing the virus removal filter. The beta‐glucan levels now lie within a range that is unlikely to cause clinically significant immunological effects. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1494–1502, 2016     

Multipronged approach to managing beta‐glucan contaminants in the downstream process: Control of raw materials and filtration with charge‐modified nylon 6,6 membrane filters

(1→3)‐β‐d ‐Glucans (beta‐glucans) have been found in raw materials used in the manufacture of recombinant therapeutics. Because of their biological activity, beta‐glucans are considered process contaminants and consequently their level in the product needs to be controlled. Although beta‐glucans introduced into the cell culture process can readily be removed by bind‐and‐elute chromatography process steps, beta‐glucans can also be introduced into the purification process through raw materials containing beta‐glucans as well as leachables from filters made from cellulose. This article reports a multipronged approach to managing the beta‐glucan contamination in the downstream process. Raw material screening and selection can be used to effectively limit the level of beta‐glucan introduced into the downstream process. Placement of a cellulosic filter upstream of the last bind‐and‐elute column step or effective preuse flushing can also limit the level of contaminant introduced. More importantly, this article reports the active removal of beta‐glucan from the downstream process when necessary. It was discovered that the Posidyne^® filter, a charge‐modified nylon 6,6 membrane filter, was able to effectively remove beta‐glucans from buffers at relatively low pH and salt concentrations. An approach of using low beta‐glucan buffer components combined with filtration of the buffer with a Posidyne membrane has been successfully demonstrated at preparative scale. Additionally, the feasibility of active removal of beta‐glucan from in‐process product pools by Posidyne membrane filtration has also been demonstrated. Based on the data presented, a mechanism for binding is proposed, as well as a systematic approach for sizing of the Posidyne filter. © 2013 American Institute of Chemical Engineers Biotechnol. Prog., 29:672–680, 2013     

Modelling and quantification of Eco-functional Index: The concept and applications of eco-functional assessment

This research work deals with the development of a novel framework to assess any product/textile material in terms of its eco-functional characteristics and to derive the Eco-functional Index from various sub-indices. Functional and ecological properties combined with consumer behaviour are the key aspects influencing the implications for the environment of any product, including textile products. Functional, ecological properties and consumer behaviour are currently treated as individual issues by consumers, business people and also industry. In fact, they are interrelated and interactive; their interaction is at the heart of this research. To date, no systematic study has been reported in the literature addressing the interrelation and interaction of these aspects. This study makes an attempt to combine these aspects in a single platform termed “Eco-functional Assessment”. This research work discusses the concept of eco-functional assessment and demonstrates the applications of the concept by considering shopping bags used for grocery purposes as an example. The primary aim of this research study is to fill the knowledge gaps by establishing a theoretical framework of eco-functional assessment, which has not been reported in the literature to date. An eco-functional model was developed with four inputs (raw materials, process of manufacture, functional properties and ecological properties) and five outputs (quality, functionality, 3Rs, human impact, environmental impact) to quantify the Eco-functional Index of any product/textile material. These inputs and outputs and their interrelation can provide a profile of the essential characteristics for the eco-functional assessment of any textile product. With the aid of the eco-functional model where the values from the discussed aspects are synthesized, eco-functional capacities of any product can be assessed and an “Eco-functional” Index can be assigned to any product. Eco-functional Index would be of primary importance to designers, manufacturers and so on. 23 samples made out of different types of shopping bags were assessed in terms of their eco-functional properties and the eco-functional score of each bag was evaluated and the results are presented. The results of the eco-functional assessment reveal the importance of every aspect of a product to meet the requirements of eco-functional assessment.     

Optimization of a Saccharomyces cerevisiae fermentation process for production of a therapeutic recombinant protein using a multivariate bayesian approach

Various approaches have been applied to optimize biological product fermentation processes and define design space. In this article, we present a stepwise approach to optimize a Saccharomyces cerevisiae fermentation process through risk assessment analysis, statistical design of experiments (DoE), and multivariate Bayesian predictive approach. The critical process parameters (CPPs) were first identified through a risk assessment. The response surface for each attribute was modeled using the results from the DoE study with consideration given to interactions between CPPs. A multivariate Bayesian predictive approach was then used to identify the region of process operating conditions where all attributes met their specifications simultaneously. The model prediction was verified by twelve consistency runs where all batches achieved broth titer more than 1.53 g/L of broth and quality attributes within the expected ranges. The calculated probability was used to define the reliable operating region. To our knowledge, this is the first case study to implement the multivariate Bayesian predictive approach to the process optimization for the industrial application and its corresponding verification at two different production scales. This approach can be extended to other fermentation process optimizations and reliable operating region quantitation. © 2012 American Institute of Chemical Engineers Biotechnol. Prog., 28: 1095–1105, 2012     

Guidance for performing multivariate data analysis of bioprocessing data: Pitfalls and recommendations

Biotech unit operations are often characterized by a large number of inputs (operational parameters) and outputs (performance parameters) along with complex correlations among them. A typical biotech process starts with the vial of the cell bank, ends with the final product, and has anywhere from 15 to 30 such unit operations in series. Besides the above‐mentioned operational parameters, raw material attributes can also impact process performance and product quality as well as interact among each other. Multivariate data analysis (MVDA) offers an effective approach to gather process understanding from such complex datasets. Review of literature suggests that the use of MVDA is rapidly increasing, fuelled by the gradual acceptance of quality by design (QbD) and process analytical technology (PAT) among the regulators and the biotech industry. Implementation of QbD and PAT requires enhanced process and product understanding. In this article, we first discuss the most critical issues that a practitioner needs to be aware of while performing MVDA of bioprocessing data. Next, we present a step by step procedure for performing such analysis. Industrial case studies are used to elucidate the various underlying concepts. With the increasing usage of MVDA, we hope that this article would be a useful resource for present and future practitioners of MVDA. © 2014 American Institute of Chemical Engineers Biotechnol. Prog., 30:967–973, 2014     

Toward an Overall Analytical Framework for the Integrated Sustainability Assessment of the Production and Supply of Raw Materials and Primary Energy Carriers

The sustainable production and supply of raw materials (“nonenergy raw materials”) and primary energy carriers (“energy raw materials”) is a core element of many policies. The natural resource base for their production and supply, and the access thereto, are limited. Moreover, raw material supply is high on environmental and social impact agendas as well. A broad, quantitative framework that supports decision makers is recommended so as to make use of raw materials and primary energy carriers more sustainably. First, this article proposes a holistic classification of raw materials and primary energy carriers. This is an essential prerequisite for developing an integrated sustainability assessment framework (ISAF). Indeed, frequently, only a subset of raw materials and primary energy carriers are considered in terms of their source, sector, or final application. Here, 85 raw materials and 30 primary energy carriers overall are identified and grouped into seven and five subgroups, respectively. Next, this article proposes a quantitative ISAF for the production and supply of raw materials and primary energy carriers, covering all the sustainability pillars. With the goal of comprehensiveness, the proposed ISAF integrates sustainability issues that have been covered and modeled in quite different quantitative frameworks: ecosystem services; classical life cycle assessment (LCA); social LCA; resource criticality assessment; and particular international concerns (e.g., conflict minerals assessment). The resulting four areas of concerns (i.e., environmental, technical, economic, and social/societal) are grouped into ten specific sustainability concerns. Finally, these concerns are quantified through 15 indicators, enabling the quantitative sustainability assessment of the production and supply of raw materials and primary energy carriers.     

Binding properties of palmatine to DNA: spectroscopic and molecular modeling investigations

Palmatine, an isoquinoline alkaloid, is an important medicinal herbal extract with diverse pharmacological and biological properties. In this work, spectroscopic and molecular modeling approaches were employed to reveal the interaction between palmatine and DNA isolated from herring sperm. The absorption spectra and iodide quenching results indicated that groove binding was the main binding mode of palmatine to DNA. Fluorescence studies indicated that the binding constant (K) of palmatine and DNA was ~ 10⁴ L·mol^?1. The associated thermodynamic parameters, ΔG, ΔH, and ΔS, indicated that hydrogen bonds and van der Waals forces played major roles in the interaction. The effects of chemical denaturant, thermal denaturation and pH on the interaction were investigated and provided further support for the groove binding mode. In addition to experimental approaches, molecular modeling was conducted to verify binding pattern of palmatine–DNA. Copyright © 2015 John Wiley & Sons, Ltd.