Inna Ben-Anat, Global QbD Director of Teva Pharmaceuticals

Meet Inna Ben-Anat, Global QbD Director of Teva Pharmaceuticals. Inna is a key thought leader in Quality by Design for generics. Ben-Anat, InnaASSOCIATE DIRECTOR, HEAD OF QDD STRATEGY | TEVA PHARMACEUTICALSAssociate Director, Head of QbD Strategy Chemical Engineer with a degree in Quality Assurance and Reliability (Technion-Israel Institute of Technology). QbD Strategy Leader at Teva (USA). Headed the implementation of a global QbD training programme. More than 12 years of pharmaceutical development experience. Inna Ben-Anat Inna Ben-Anat is a Quality by Design (QbD) Strategy Leader in Teva Pharmaceuticals USA. In this role, Inna has implemented global QbD training program, and is supporting R&D teams in developing Quality by Design strategies, optimizing formulations and processes and assisting develop product specifications. Additionally, Inna supports Process Engineering group with process optimization during scale-up and supports Operations in identification and resolution of any technical issues. Inna has extensive expertise in process development, design of experiments for process and product optimization, and statistical data analysis, and has more than 10 years of pharmaceutical development experience. Inna received her BSc degree in Chemical Engineering and ME degree in Quality Assurance and Reliability in Engineering from Technion-Israeli Institute of Technology. Teva Pharmaceutical Industries Ltd. 5 Basel Street PO Box 3190 Petah Tiqwa , 4951033 Israel



Assc. Director, Global QbD Strategy

Teva Pharmaceuticals

May 2013 – Present (2 years 2 months)

QbD Strategy Leader

Teva Pharmaceuticals

March 2011 – Present (4 years 4 months)

Analytical R&D

TransPharma Medical

2001 – 2005 (4 years)

Employment History

  • Director, Global QbD Strategy and Product Robustness
    Teva Pharmaceutical Industries Ltd.
  • QbD Strategy Leader
    Teva Pharmaceutical Industries Ltd.
  • Analytical Research and Development Manager
    TransPharma Medical Ltd


Petah Tikva, ISRAEL

  1. Petah Tikva – Wikipedia, the free encyclopedia

    Petah Tikva (Hebrew: פֶּתַח תִּקְוָה, IPA: [ˈpetaχ tikˈva], “Opening of Hope”) known as Em HaMoshavot (“Mother of the Moshavot”), is a city in the Central …

Khayim Ozer Street, Petah Tikva, Israel

Understanding Pharmaceutical Quality by Design 2014 Lawrence X. Yu, Gregory Amidon, Mansoor A. Khan, Stephen W. Hoag, James Polli, G. K. Raju, Janet Woodcock
A quality by design study applied to an industrial pharmaceutical fluid bed granulation 2012 Vera Lourenço, Dirk Lochmann, Gabriele Reich, José C. Menezesa, Thorsten Herdling, Jens Schewitz
Application of the quality by design approach to the drug substance manufacturing process of an Fc fusion protein: towards a global multi-step design space. 2012 Eon-duval A, Valax P, Solacroup T, Broly H, Gleixner R, Strat CL, Sutter J.
Instrumented roll technology for the design space development of roller compaction process 2012 Vishwas V. Nesarikar, Nipa Vatsaraj, Chandrakant Patel, William Early, Preetanshu Pandey, Omar Sprockel, Zhihui Gao, Robert Jerzewski, Ronald Miller, Michael Levin
Rapid exploration of curing process design space for production of controlled-release pellets 2012 Katja Kristan andMatej Horvat
Automating push-through force testing of blister packs 2011 Jha, Salil
Quality by Design for ANDAs: An Example for Modified Release Dosage Forms 2011
QbD Status Update Generic Drugs 2011 Susan Rosencrance
Design-for-Six-Sigma for Development of a Bioprocess Quality-by-Design Framework 2011 Junker B, Kosinski M, Geer D, Mahajan R, Chartrain M, Meyer B, Dephillips P, Wang Y, Henrickson R, Ezis K, Waskiewicz M.
Quality by design and process analytical technology for sterile products–where are we now? 2010 Riley BS, Li X.
Development of quality-by-design analytical methods 2010 Frederick G. Vogt andAlireza S. Kord
Roadmap for implementation of quality by design (QbD) for biotechnology products. 2009 Rathore AS
Quality by Design for Biopharmaceuticals: Principles and Case Studies 2009 Anurag S. Rathore (Editor), Rohin Mhatre (Editor)
Quality by design for biopharmaceuticals 2009 Anurag S Rathore & Helen Winkle
Formulation optimization of long-acting depot injection of aripiprazole by using D-optimal mixture design 2009 Nahata T, Saini TR.
Quality by Design: Get to the production scale quickly and successfully with Design of Experiments 2009 Norbert Pöllinger, Stefanie Feiler, Philippe Solot,
Pharmaceutical quality by design: product and process development, understanding, and control. 2008 Yu LX
Quality by design for biopharmaceuticals 2009 Anurag S Rathore1 & Helen Winkle2
Why quality-by-design should be on the executive team’s agenda 2009 Ted Fuhr, Michele Holcomb, Paul Rutten
BioProcess Control: What the Next 15 Years Will Bring 2008 Michael Boudreau, Emerson Process Management, Inc. and Trish Benton, Broadley-James Corp.
QbD for Better Method Validation and Transfer 2010 Phil Nethercote, Phil Borman, Tony Bennett, GSK; Gregory Martin, Complectors Consulting LLC, and Pauline McGregor, PMcG Consulting
A Quality-by-Design Methodology for Rapid LC Method Development (Parts I, II and III) 2008 Ira Krull, Michael Swartz, Joseph Turpin, Patrick H. Lukulay, Richard Verseput;;
What Your ICH Q8 Design Space Needs: A Multivariate Predictive Distribution 2010 John J. Peterson, GlaxoSmithKline Pharmaceuticals
Quality-by-Design Using a Gaussian Mixture Density Approximation of Biological Uncertainties 2010 N. Rossner, Th. Heine, R. King
An Example of Utilizing Mechanistic and Empirical Modeling in Quality by Design 2010 Daniel M. Hallow, Boguslaw M. Mudryk, Alan D. Braem, Jose E. Tabora, Olav K. Lyngberg, James S. Bergum, Lucius T. Rossano, Srinivas Tummala
Process modeling and control in drug development and manufacturing 2010 Edited by Cynthia Oksanen and Salvador García-Muñoz
Bioprocesses: Modeling needs for process evaluation and sustainability assessment 2010 Concepción Jiménez-González, John M. Woodley
Computer-aided molecular design using the Signature molecular descriptor: Application to solvent selection 2009 Derick C. Weis , Donald P. Visco
Process modeling and optimization of batch fractional distillation to increase throughput and yield in manufacture of active pharmaceutical ingredient (API) 2010 Yubo Yang, , Rosalin Tjia
The use of modeling in spray drying of emulsions and suspensions accelerates formulation and process development 2009 James W. Ivey, Reinhard Vehring
Pharmaceutical process/equipment design methodology case study: Cyclone design to optimize spray-dried-particle collection efficiency 2010 Lisa J. Graham, Rebecca Taillon, Jim Mullina, Trevor Wigle
Practical application of roller compaction process modeling 2010 Gavin Reynolds, Rohit Ingale, Ron Roberts, Sanjeev Kothari, Bindhu Gururajan
Understanding variation in roller compaction through finite element-based process modeling 2010 John C. Cunninghama, Denita Winstead, Antonios Zavaliangos
Optimizing the design of eccentric feed hoppers for tablet presses using DEM 2010 William R. Ketterhagen, Bruno C. Hancock
Temperature and density evolution during compaction of a capsule shaped tablet 2010 Gerard R. Klinzing, Antonios Zavaliangos, John Cunningham, Tracey Mascaro, Denita Winstead
Drug product modeling predictions for scale-up of tablet film coating—A quality by design approach 2010 Andrew Prpich, Mary T. am Ende, Thomas Katzschner, Veronika Lubczyk, Holger Weyhers, Georg Bernhard
Handling uncertainty in the establishment of a design space for the manufacture of a pharmaceutical product 2010 Salvador García-Muñoz, , Stephanie Dolph, Howard W. Ward II
ICAS-PAT: A software for design, analysis and validation of PAT systems 2009 Ravendra Singh, Krist V. Gernaey, Rafiqul Gania,
An ontological knowledge-based system for the selection of process monitoring and analysis tools 2010 Ravendra Singh, Krist V. Gernaey, Rafiqul Gani
An ontological framework for automated regulatory compliance in pharmaceutical manufacturing 2009 M. Berkan Sesen, Pradeep Suresh, René Banares-Alcantara, Venkat Venkatasubramanian

Key steps in implementation of QbD for a biotech product…….Quality by design for biopharmaceuticals

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Identifying target product profile (TPP). TPP has been defined as a “prospective and dynamic summary of the quality characteristics of a drug product that ideally will be achieved to ensure that the desired quality, and thus the safety and efficacy, of a drug product is realized”. This includes dosage form and route of administration, dosage form strength(s), therapeutic moiety release or delivery and pharmacokinetic characteristics (e.g., dissolution and aerodynamic performance) appropriate to the drug product dosage form being developed and drug product-quality criteria (e.g., sterility and purity) appropriate for the intended marketed product. The concept of TPP in this form and its application is novel in the QbD paradigm.

Identifying CQAs. Once TPP has been identified, the next step is to identify the relevant CQAs. A CQA has been defined as “a physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality”10. Identification of CQAs is done through risk assessment as per the ICH guidance Q9 . Prior product knowledge, such as the accumulated laboratory, nonclinical and clinical experience with a specific product-quality attribute, is key in making these risk assessments. Such knowledge may also include relevant data from similar molecules and data from literature references. Taken together, this information provides a rationale for relating the CQA to product safety and efficacy. The outcome of the risk assessment would be a list of CQAs ranked in order of importance. Use of robust risk assessment methods for identification of CQAs is novel to the QbD paradigm.

Defining product design space. After CQAs for a product have been identified, the next step is to define the product design space (that is, specifications for in-process, drug substance and drug product attributes). These specifications are established based on several sources of information that link the attributes to the safety and efficacy of the product, including, but not limited to, the following:

  • Clinical design space
  • Nonclinical studies with the product, such as binding assays, in vivo assays and in vitro cell-based assays
  • Clinical and nonclinical studies with similar platform products
  • Published literature on other similar products
  • Process capability with respect to the variability observed in the manufactured lots

The difference between the actual experience in the clinic and the specifications set for the product would depend on our level of understanding of the impact that the CQA under consideration can have on the safety and efficacy of the product. For example, taking host cell proteins as a CQA, it is common to propose a specification that is considerably broader than the clinical experience. This is possible because of a greater ability to use data from other platform molecules to justify the broader specifications. On the other hand, in the case of an impurity that is unique to the product, the specifications would rely solely on clinical and nonclinical studies.

In QbD, an improved understanding of the linkages between the CQA and safety and efficacy of the product is required. QbD has brought a realization of the importance of the analytical, nonclinical and animal studies in establishing these linkages and has led to the creation of novel approaches.

Defining process design space. The overall approach toward process characterization involves three key steps. First, risk analysis is performed to identify parameters for process characterization. Second, studies are designed using design of experiments (DOE), such that the data are amenable for use in understanding and defining the design space. And third, the studies are executed and the results analyzed to determine the importance of the parameters as well as their role in establishing design space.

Failure mode and effects analysis (FMEA) is commonly used to assess the potential degree of risk for every operating parameter in a systematic manner and to prioritize the activities, such as experiments, necessary to understand the impact of these parameters on overall process performance. A team consisting of representatives from process development, manufacturing and other relevant disciplines performs an assessment to determine severity, occurrence and detection. The severity score measures the seriousness of a particular failure and is based on an estimate of the severity of the potential failure effect at a local or process level and the potential failure effect at end product use or patient level. Occurrence and detection scores are based on an excursion (manufacturing deviation) outside the operating range that results in the identified failure. Although the occurrence score measures how frequently the failure might occur, the detection score indicates the probability of timely detection and correction of the excursion or the probability of detection before end product use. All three scores are multiplied to provide a risk priority number (RPN) and the RPN scores are then ranked to identify the parameters with a high enough risk to merit process characterization.  FMEA outcome for a process chromatography step in a biotech process. RPN scores are calculated and operating parameters with an RPN score >50 are characterized using a qualified scaled-down model. For the case study presented here, these include gradient slope, temperature, flow rate, product loading, end of pool collection, buffer A pH, start of pool collection, volume of wash 1, buffer B pH, buffer C pH and bed height. Process characterization focused on parameters such as temperature, that have a high impact on the process (severity = 6), occur frequently in the manufacturing plant (occurrence = 6) and are difficult to quickly correct if detected (detection = 7). In contrast, parameters such as equilibration volume, with a low impact on the process (severity = 3), low occurrence (occurrence = 2) and a limited ability to detect and correct (detection = 5), were not examined in process characterization.

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