Drug Metabolism GUIDE TO INNOVATION

Drug Metabolism GUIDE TO INNOVATION

Drug Metabolism Analysis with Confidence Pharmaceutical and biopharmaceutical companies have leveraged advancements in basic science perhaps more than any other industry. With the advent of whole genome sequencing, sophisticated analysis of metabolic pathways, and exponential improvements in computer processing, R&D organizations have expanded their drug portfolio focus from small molecules to biotherapeutics. To ensure continued success in drug development, the Industry has been challenged to design and deliver targeted new medicines with higher efficiency at a faster pace than ever before. As a result, better and faster information is required to develop a fundamental understanding of the target compound s efficiency as a therapeutic and its metabolic profile for potential toxicity. Partnering with customers by understanding their challenges, collaborating on ideas, and ultimately creating cutting-edge solutions that provide new ways to address these new challenges in drug metabolism is our priority. The following compendium includes key solutions for drug metabolism and, more importantly, describes in detail work done by, and in collaboration with, our customers. Your success is our success, and the AB SCIEX team will partner with you to overcome some of the biggest challenges of drug metabolism analysis, now and into the future. Gary Impey, PhD Director, Pharma/CRO MS Business RUO-MKT-01-1583-A

For Research Use Only. Not for use in diagnostic procedures. Warrington,UK Redwood Shores & Dublin, CA, U.S.A. Concord, ONT,Canada Framingham, MA, U.S.A. Paris, France Darmstadt, Germany Milan, Italy Seoul, S. Korea Tokyo, Japan New Delhi, India Shanghai, China Singapore Melbourne, Australia AB SCIEX global associates and sites 1,500+ associates worldwide 260+ hold PhDs or other advanced degrees 14 operating sites Global sales and service teams 7 global demo labs

Xenobiotic Metabolism Workflows in Drug Discovery and Development Introduction 6-15 Xenobiotic Metabolism Workflows in Drug Discovery and Development Contents Technology Spotlight Technology Drives High Performance in Biomolecular Mass Spectrometry High Resolution Time-of-Flight MS PCVG: A Powerful Algorithm for Automating Comprehensive Xenobiotic Metabolite Identification Early Discovery DiscoveryQuant Software 2.0: The Definitive Solution for LC/MS/MS Early-ADME Workflows Confirmation of In Vitro Nefazodone Metabolites Using the Superior Fragmentation of the QTRAP 5500 LC/MS/MS System Breakthrough Productivity for ADME Studies Using the AB SCIEX TripleTOF 5600 System 16-27 16-19 20-23 24-27 28-43 28-31 32-37 38-43 RUO-MKT-01-1583-A

For Research Use Only. Not for use in diagnostic procedures. 44-67 44-47 48-51 52-57 58-61 62-67 Late Stage Discovery Rapid Metabolite Identification Using MetabolitePilot Software and the TripleTOF 5600 System In Vivo Metabolic Profiling of Carbamazepine Using the QTRAP 5500 System and LightSight Software 2.2 Comprehensive Detection of Metabolites Using Polarity Switching Data Collection with the QTRAP 5500 LC/MS/MS System Simultaneous Pharmacokinetic Profiling and Automated Metabolite Identification Using the AB SCIEX TripleTOF 5600 System and MetabolitePilot Software Metabolite Identification with the QTRAP 5500 LC/MS/MS System: Sensitivity, Selectivity, Speed, and Unique Workflows 68-85 68-71 72-77 78-81 82-85 Definitive Metabolite ID Simultaneous Metabolite Identification and Quantitation with UV Data Integration Using LightSight Software 2.2 Differential Mobility Spectrometry for Quantitative and Qualitative Applications in Pharmaceutical Workflows Solving Bottlenecks in Metabolite Identification Using TripleTOF Systems and MetabolitePilot Software Removing Bottlenecks in Metabolite ID Data Analysis with MetabolitePilot Software

INTRODUCTION Xenobiotic Metabolism Workflows in Drug Discovery and Development Laura Baker 1, Suma Ramagiri 2 1 Contract Technical Writer at AB SCIEX, Pittsburgh, PA, 2 AB SCIEX, Concord, Canada To stay competitive in the field of modern therapeutics, the pharmaceutical industry must deliver increasingly potent and targeted new medicines at an ever faster pace. The discovery and development process for new drugs is a high-risk venture, where numerous prospective candidates are screened, but only one in five compounds in the development stage will successfully complete clinical research studies to become a marketable drug (Figure 1). Key to this process is assessing a compound s suitability as a drug, and defining its metabolic profile is essential to ensure that its downstream metabolites are not toxic. Rapid feedback on metabolite composition during early stages of drug development means a more effective screening process, preventing the costly possibility of an unsuitable candidate progressing too far down the development pipeline. Characterization of the body s metabolic response to a drug has evolved from a late-stage regulatory requirement to an essential and pivotal finding in the discovery stage that must be attained quickly alongside in vitro biological screening results. This shift towards earlier metabolite profiling during the drug discovery process has impacted pharmaceutical discovery workflows, requiring evaluation of drug metabolism properties at every step (Figure 2). In the early discovery stage, metabolic stability and its influence on pharmacological response is explored at a more cursory level relying on in vitro analysis to help steer the selection of lead candidates. A drug must have appropriate bioavailability and efficacy over a desired interval of time to be successful, leading to increased efforts to characterize metabolites early in the discovery stage and to provide critical soft spot analysis for lead optimization. Later stage metabolic profiling may reveal that in vitro metabolites are different from those obtained in vivo; furthermore, in vivo metabolites may vary between animal models. 1 Comparison of metabolites from these screens early in the process allows for the selection of appropriate animal models for toxicology screens. 2 In the development stage, application for new drug status with the FDA mandates that a high level of metabolic characterization be delivered, including in-depth structural elucidation and metabolite identification. 4 Metabolites may clear from the body differently from the parent drug and act by differing enzymatic mechanisms; thus, it is ideal to have detailed pharmacokinetic and absolute quantitative information in hand before clinical studies begin. Appreciating that the safety of patients is compromised if the toxicological effects of a new drug are not well understood, a drug candidate s metabolic fingerprint can constructively shape the design of a clinical trial. 2 The substantial impact that metabolic parameters have on a drug s continued success through the development pipeline and the effectiveness of providing in-depth metabolic information earlier in this process highlights the increasing need for high-throughput bioanalytical techniques that rapidly deliver comprehensive metabolic profiles. In the past decade, metabolite identification workflows have evolved fundamentally due to breakthrough advances in mass spectrometry technologies. 4 This shift has driven the movement from analytical compound-based mass spectrometry workflows, which were often multi-step and multi-injection, towards more generic workflows that focus on extracting metabolite structural information using post-acquisition processing of unbiased, non-targeted mass spectral data (all-in-one approach). 4 Traditional Figure 1: Drug discovery and development pipeline. 6 RUO-MKT-01-1583-A DRUG METABOLISM www.absciex.com

For Research Use Only. Not for use in diagnostic procedures. precursor ions are used to filter unbiased scan data to arrive at relevant peaks related to parent drug material. 4-6 (A complete lists of filters types are listed or reviewed in the given references. 2,3 ) Emerging to further expand the tool box for metabolite identification are strategies for real-time filtering of non-targeted data during acquisition that can keep pace with the shorter time scales during ultra-high pressure liquid chromatography (UHPLC) separations and identify drug-related material as it is being acquired. 2,3 INTRODUCTION Figure 2: Metabolite profiling tasks required for each stage of the drug discovery process. phase I and II metabolite detection often involves two separate injections, including a full scan of the sample and then a second scan to perform MS/MS of pre-selected peaks that correspond to potential parent drug metabolites. 2,4 This dual injection approach is very laborious and further consumes valuable sample; however, the primary limiting factor is the processing of the extensive data files generated during non-targeted fragmentation of every component included within the sample. 3 MS/MS fragment ion interpretation and assignment of new metabolite structures is very labor-intensive and typically conducted using non-integrated, independent software packages, adding complexity to an already daunting process. Increasing to the challenge, drug studies are frequently carried out in biological fluids or tissues, so low-level metabolites from clinically-relevant doses of the parent drug must be detected against a backdrop of competing ions that can suppress signals of interest. 3 For the pharmaceutical scientist, the key to overcoming this data bottleneck at the discovery stage is to rapidly find, identify, and confirm low-level metabolites in complex matrices using highly sensitive and selective automated processes and obtain this data from just a single injection. This resource on xenobiotic metabolism workflows explores fast, efficient mass spectrometry techniques that meet the challenge of high-throughput metabolite identification and quantitation during the drug discovery and development process. With the advent of a new generation of high resolution mass spectrometers (HRMS), including Fourier transform mass spectrometry and hybrid triple quadrupole/time-of-flight instruments, non-targeted, unbiased detection of all metabolites, even unpredicted, can be carried out in just one run. 2,4 What has emerged as the new standard for metabolite studies is the coupling of high resolution, accurate mass instruments with effective computational strategies for filtering the large data sets from all-in-one fragmentation approaches, easing interpretation and increasing throughput. 2-5 Newer workflows emphasize post-acquisition analysis of data files, where metabolite predictions based on known biotransformation activities, intensity cut-offs, neutral loss, mass defects, and Herein we present an extensive resource composed of drug metabolite identification and quantitation studies that were performed by our own scientists, customers, and collaborators, which reveal how highly selective and sensitive detection of lowlevel metabolites can be carried out on state-of-the-art AB SCIEX mass spectrometers, even in the presence of the high background noise from biological samples. Section 1: We explore high-throughput ADMET (adsorption, distribution, metabolism, excretion and toxicity) screening workflows that are employed in early drug discovery. Efficiency is essential due to the large number of compounds to be analyzed, and we focus on the automation of screening methods using DiscoveryQuant Software, as well as sensitive metabolite profiling using a predictive multiple reaction monitoring (pmrm) approach on a rapidly scanning hybrid triple quadrupole/linear ion trap mass spectrometer, the QTRAP 6500 System. Non-selective data acquisition analyzed using a real-time multiple mass defect filter (RT-MMDF) is employed on a TripleTOF 5600 System for high resolution, accurate mass detection and bioanalysis of pharmacokinetic samples. Section 2: Late stage discovery workflows are examined, evaluating technologies that integrate quantitative and qualitative processing in one run at high speeds and high sensitivity. Automated, rapid metabolite identification conducted on the TripleTOF 5600 system with MetabolitePilot Software enables high-throughput structure analysis and quantitation. Metabolite data captured using non-targeted SWATH Acquisition uncovers unpredicted, low-level metabolites. Workflows on the QTRAP 6500 system with LightSight Software are dedicated finding very low-level phase I metabolites and reactive phase II metabolite conjugates. Section 3: Definitive metabolite identification during DMPK (drug metabolism and pharmacokinetics) workflows is highlighted, examining how mass accuracy and mass-defect-triggered, informationdependent acquisition (IDA) enable assisted structural characterization of even minor metabolites in complex matrices with the TripleTOF 5600 system and MetabolitePilot Software. Metabolite signal and detection is augmented after removing competing interferences using the orthogonal filtering capacity of SelexION Differential Mobility Separation (DMS) Technology for improved selectivity in complex, in vivo samples. LightSight Software integrates external data sources (such as UV quantitation) with mass spectrometry results for improved estimation of metabolite concentration. www.absciex.com DRUG METABOLISM 7

INTRODUCTION Title Article Highlights Citation Software automation tools for increased throughput metabolic soft-spot identification Application of software automation tools for rapidly identifying metabolites using mass defect filtering and HRMS instruments Zelesky V, Schneider, R, Janiszewski J, Zamora I, Ferguson in early drug discovery Review of MetabolitePilot Software and Mass-MetaSite Software workflows for finding peaks, identifying metabolites, and elucidating structures J, Troutman M. Bioanalysis. 2013; 5(10): 1165-1179. Focus was on accuracy and throughput for the localization of the primary soft spots on firstgeneration metabolites of proprietary compounds Comparison of information-dependent acquisition, SWATH Acquisition, and MS All Techniques in Evaluation of methods for acquiring MS/MS data using an HRMS instrument for metabolite ID in microsomes and urine Zhu Z, Chen Y, Subramanian R. Analytical Chemistry. 2014; metabolite identification study employing ultrahigh-performance liquid chromatography Comparison of IDA, SWATH Acquisition, and MS All data acquisition methodologies using eight non-proprietary compounds that produced a diverse array of metabolites 86(2):1202-9. quadrupole time-of-flight mass spectrometry SWATH Acquisition and MS All methods surpassed IDA hit rates, triggering MS/MS for all metabolites, while IDA methods produced superior MS/MS data for facilitation of structural assignment. MS/MS spectra quality is highly dependent on the Q1 selection window width (and thus the acquisition method). Identification of urinary metabolites of imperatorin with a single run on an LC/Triple TOF system based on multiple mass defect filter data acquisition and multiple data mining techniques Identification of metabolites of deoxyschizandrin in rats by UPLC-Q-TOF-MS/MS based on multiple mass defect filter data acquisition and multiple data processing techniques Standardized workflows for increasing efficiency and productivity in discovery stage bioanalysis Bioactivation of sitaxentan in liver microsomes, hepatocytes, and expressed human P450s with characterization of the glutathione conjugate by liquid chromatography tandem mass spectrometry Drug metabolite profiling and identification by high-resolution mass spectrometry A generic, single-injection approach for detecting in vivo metabolites, including low-level, of an herbal Chinese medicine using HRMS instrumentation and a single injection protocol Data acquisition was conducted using real-time multiple mass defect filters (MMDF) combined with dynamic background subtraction to identify urinary metabolites. Low-level metabolites were identified from high background and excess endogenous components by combining HRMS data mining techniques (XIC, MDF, PIF, and NLF). Structures for 44 phase I and 7 phase II metabolites were reported using this powerful, integrated approach. Development of a novel and efficient strategy for screening the in vivo metabolites of an herbal Chinese medicine using HRMS instrumentation and a single injection protocol Filters such as MMDF and DBS were applied on-line during data acquisition, and multiple post data-acquisition filters (XIC, MDF, PIF, and NLF) were combined to obtain 51 phase I and II metabolites in rat urine and bile. MetabolitePilot Software identified metabolites, and structure elucidation was enabled by accurate mass information, biotransformation knowledge, and fragmentation patterns. A unique, Clog P value was assigned to compounds to distinguish between multiple isomers, which was based on varying retention times for isomers. Discussion of standardized discovery LC-MS workflows for high-throughput, small-molecule bioanalysis and the efficiency gains after implementation Bioanalytical processes (compound tuning, LC method development, analytical acceptance criteria, automated sample preparation, sample analysis platforms, data processing, and data reporting) were harmonized across multiple research sites. Reducing time and resources on routine bioanalysis has allowed for more challenging studies and development of future research. Identification of a reactive metabolite and its structure for an endothelin-a receptor antagonist withdrawn for idiosyncratic drug toxicity Characterization of an in vitro GSH-conjugate in liver microsomes from wide array of mammals using hybrid triple quadrupole/time-of-flight mass spectrometry full scan data and product ion spectra Reactive metabolite inhibition of a specific P450 isoform was demonstrated through competitive and time-dependent assays and proposed as a mechanism for drug toxicity. Overview of HRMS acquisition methods (both targeted and non-targeted) and data mining techniques (mass defect, product ion, isotope pattern filters, and background subtraction) Review of single HRMS platforms with the capacity for multiple metabolite ID tasks Future developments for metabolite ID on HRMS instruments Qiao S, Shi X, Shi R, Liu M, Liu T, Zhang K, Wang Q, Yao M, Zhang L. Anal Bioanal Chem. 2013; 405: 6721-6738. Liu M, Zhao S, Wang Z, Wang Y, Liu T, Song L, Wang C, Wang H, Tu P. Journal of Chromatography B. 2014; 949-950:115-126. Bateman KP, Cohen L, Emary B, Pucci V. Bioanalysis. 2013; 5(14): 1783-1794. Erve JCL, Gauby S, Maynard, Jr. JW, Svensson MA, Tonn G, Quinn KP. Chemical Research in Toxicology. 2013; 26: 926-936. Zhu, M, Zhang, H, Humphreys WG. J Biol Chem. 2011; 286 (29): 25419-25425. *These articles were reprinted with permission in the first 30 copies of this resource. Table 1: Selected citations for further reading on metabolism workflows in drug discovery and development 8 RUO-MKT-01-1583-A DRUG METABOLISM www.absciex.com

For Research Use Only. Not for use in diagnostic procedures. INTRODUCTION Title Article Highlights Citation Software automation tools for increased throughput metabolic soft-spot identification Application of software automation tools for rapidly identifying metabolites using mass defect filtering and HRMS instruments Zelesky V, Schneider, R, Janiszewski J, Zamora I, Ferguson in early drug discovery Review of MetabolitePilot Software and Mass-MetaSite Software workflows for finding peaks, identifying metabolites, and elucidating structures J, Troutman M. Bioanalysis. 2013; 5(10): 1165-1179. Focus was on accuracy and throughput for the localization of the primary soft spots on firstgeneration metabolites of proprietary compounds Comparison of information-dependent acquisition, Evaluation of methods for acquiring MS/MS data using an HRMS instrument for metabolite ID in Zhu Z, Chen Y, Subramanian SWATH Acquisition, and MS All Techniques in microsomes and urine R. Analytical Chemistry. 2014; metabolite identification study employing Comparison of IDA, SWATH Acquisition, and MS All data acquisition methodologies using eight 86(2):1202-9. ultrahigh-performance liquid chromatography non-proprietary compounds that produced a diverse array of metabolites quadrupole time-of-flight mass spectrometry SWATH Acquisition and MS All methods surpassed IDA hit rates, triggering MS/MS for all metabolites, while IDA methods produced superior MS/MS data for facilitation of structural assignment. MS/MS spectra quality is highly dependent on the Q1 selection window width (and thus the acquisition method). Identification of urinary metabolites of imperatorin with a single run on an LC/Triple TOF system based A generic, single-injection approach for detecting in vivo metabolites, including low-level, of an herbal Chinese medicine using HRMS instrumentation and a single injection protocol Qiao S, Shi X, Shi R, Liu M, Liu T, Zhang K, Wang Q, Yao M, Zhang on multiple mass defect filter data acquisition and multiple data mining techniques Data acquisition was conducted using real-time multiple mass defect filters (MMDF) combined with dynamic background subtraction to identify urinary metabolites. L. Anal Bioanal Chem. 2013; 405: 6721-6738. Low-level metabolites were identified from high background and excess endogenous components by combining HRMS data mining techniques (XIC, MDF, PIF, and NLF). Structures for 44 phase I and 7 phase II metabolites were reported using this powerful, integrated approach. Identification of metabolites of deoxyschizandrin in rats by UPLC-Q-TOF-MS/MS based on multiple Development of a novel and efficient strategy for screening the in vivo metabolites of an herbal Chinese medicine using HRMS instrumentation and a single injection protocol Liu M, Zhao S, Wang Z, Wang Y, Liu T, Song L, Wang C, Wang H, mass defect filter data acquisition and multiple data processing techniques Filters such as MMDF and DBS were applied on-line during data acquisition, and multiple post data-acquisition filters (XIC, MDF, PIF, and NLF) were combined to obtain 51 phase I and II metabolites Tu P. Journal of Chromatography B. 2014; 949-950:115-126. in rat urine and bile. MetabolitePilot Software identified metabolites, and structure elucidation was enabled by accurate mass information, biotransformation knowledge, and fragmentation patterns. A unique, Clog P value was assigned to compounds to distinguish between multiple isomers, which was based on varying retention times for isomers. Standardized workflows for increasing efficiency and productivity in discovery stage bioanalysis Discussion of standardized discovery LC-MS workflows for high-throughput, small-molecule bioanalysis and the efficiency gains after implementation Bateman KP, Cohen L, Emary B, Pucci V. Bioanalysis. 2013; 5(14): Bioanalytical processes (compound tuning, LC method development, analytical acceptance criteria, 1783-1794. automated sample preparation, sample analysis platforms, data processing, and data reporting) were harmonized across multiple research sites. Reducing time and resources on routine bioanalysis has allowed for more challenging studies and development of future research. Bioactivation of sitaxentan in liver microsomes, hepatocytes, and expressed human P450s with characterization of the glutathione conjugate by liquid chromatography tandem mass spectrometry Identification of a reactive metabolite and its structure for an endothelin-a receptor antagonist withdrawn for idiosyncratic drug toxicity Characterization of an in vitro GSH-conjugate in liver microsomes from wide array of mammals using hybrid triple quadrupole/time-of-flight mass spectrometry full scan data and product ion spectra Erve JCL, Gauby S, Maynard, Jr. JW, Svensson MA, Tonn G, Quinn KP. Chemical Research in Toxicology. 2013; 26: 926-936. Reactive metabolite inhibition of a specific P450 isoform was demonstrated through competitive and time-dependent assays and proposed as a mechanism for drug toxicity. Drug metabolite profiling and identification by high-resolution mass spectrometry Overview of HRMS acquisition methods (both targeted and non-targeted) and data mining techniques (mass defect, product ion, isotope pattern filters, and background subtraction) Review of single HRMS platforms with the capacity for multiple metabolite ID tasks Zhu, M, Zhang, H, Humphreys WG. J Biol Chem. 2011; 286 (29): 25419-25425. Future developments for metabolite ID on HRMS instruments *These articles were reprinted with permission in the first 30 copies of this resource. Each section and experiment featured in this resource includes an overview of the key challenges, benefits, and features of the bioanalytical technique presented. In this way, the mass spectrometric techniques and metabolic data analysis can be put into context with other bioanalytical tools and help highlight the many advantages that LC/MS/MS offers to all stages of drug discovery and development. For further reference, Table 1 showcases the current literature, reviewing the trends and novel techniques that are behind the high-throughput innovations for detecting and identifying metabolites. Presented below are some of the key challenges and benefits of metabolite identification, alongside the innovative analytical instruments that AB SCIEX features for accelerating workflows, meeting the demands for speed, efficiency, and depth of data that are vital to intelligent and effective drug optimization. Key challenges of metabolite identification, structural assignment, and bioanalysis Analyzing very small amounts of drug-related material obscured by cellular components presents a unique challenge to a process that requires increasing levels of productivity to attain the high throughput necessary for the efficient screening of thousands of compounds (Table 2). The interfering signals from the complex matrices that harbor drug metabolites can prolong data processing times or impede the collection of adequate MS/MS information. Structural assignment is often a painstaking, manual process, relying on a biotransformation scientist s expertise to unravel the data. Capturing information on all metabolites, particularly low-level, in the early stages, is of particular importance to the selection of viable drug candidates. Missing a potentially toxic compound during the screening process due to incomplete data collection may cause valuable resources to be directed towards an unsuitable candidate that may later fail during clinical trials. (An example of a drug with a boxed warning, pulled from the market due to a previously-undetectable reactive metabolite, is given. 7 ) Testing is often completed at low, therapeutically-relevant concentrations, which hinders accurate concentration determination and requires the use of highly sensitive instrumentation. In many cases, data is obtained using multiple, non-integrated platforms making it difficult to transfer data to different applications but also making it problematic to share data with other groups. Limitations on sample availability strongly impact workflow design, and even a single re-injection to re-evaluate a peak or a missed compound requires expensive and time-consuming re-experimenation. To tackle these collective challenges, advanced LC/MS/MS techniques have been developed that are responsive to industry standards for productivity levels requisite for the successful screening of viable compounds. Key benefits of LC/MS/MS workflows for metabolite profiling AB SCIEX is dedicated to overcoming drug discovery bottlenecks and automating the metabolic profiling process by developing high-throughput, intelligent workflows that fuel drug discovery. Improvements to instrument capacity, data processing, and software design have propelled metabolite workflows forward, attaining the efficiency and productivity essential for accommodating large sample batches and the screening of thousands of compounds. (See Table 3 for an overview of the benefits of specific AB SCIEX technologies.) Crucial to quicker sample run times are fast scanning speeds and the mass spectrometer s capacity to collect data in time frames compatible with UHPLC sample elution. Data collection on orbital trapping instruments is hampered by the slower scanning speeds needed to achieve high resolution, but both the TripleTOF 5600 system and the QTRAP 6500 system have the speed and power for rapid MS and MS/MS analysis. The TripleTOF 5600 system maintains approximately 30K resolution regardless of the analysis speed and provides accurate mass data for the unambiguous assignment of elemental composition, as well as the sensitivity and linearity of a triple quadruple instrument for excellent quantitative performance. Prior to the development of hybrid machines, separate MS instruments were employed for accurate quantitation and qualitative discovery of metabolites. The high sensitivity of the QTRAP 6500 system permits metabolic profiling at physiologically relevant dosing and enables the discovery of INTRODUCTION Table 1: Selected citations for further reading on metabolism workflows in drug discovery and development SWATH Acquisition High Res XICs 25 Da counts Q1 CID retention time TOF counts m/z Figure 3: A representation of SWATH Acquisition on the TripleTOF 5600 System. 8 RUO-MKT-01-1583-A DRUG METABOLISM www.absciex.com www.absciex.com DRUG METABOLISM 9

INTRODUCTION Overlooking low-level drug metabolites omitted from MS survey scans conducted on complex biological matrices such as bile, plasma, and tissue extracts Repeat sample injections due to inadequate collection of metabolite information during initial studies leads to costly re-experimentation and re-analysis. Non-definitive metabolite identification due to inadequate acquisition of MS/MS information on minor or unpredicted metabolites Multiple, non-integrated software platforms complicate data processing, slowing metabolite identification and structure elucidation. Co-elution of isomeric or isobaric metabolites is one the major bottlenecks during definitive metabolite identification process. This may result in long chromatographic run times or even expensive column chemistry separating them before identification. Table 2: Summary of key challenges of metabolite identification hard-to-detect, low-level metabolites from just a single run. The linear ion trap scan speeds on the QTRAP 6500 system at rates of 20,000 Da/sec enhance information-dependent acquisition (IDA) coverage as well, allowing for more MS/MS scans on drugrelated peaks. Positive/negative polarization switching on both machines eliminates the need for separate runs for detecting phase II metabolites that, unlike phase I metabolites are often only detected in negative ion mode, allowing for the consolidation of run times and further gains in workflow efficiency. Improvements to DiscoveryQuant Software have automated many aspects of method development and sample processing. The integrated, easy-to-use software provides automated methodbuilding templates, auto sampler support, and batch processing for fast optimization of sample acquisition without requiring extensive operator input for every compound. Multiple aspects of data collection are consolidated into one software package, so that method building, data acquisition, and processing can occur on one platform, streamlining data review and saving time. Data processing platforms (MetabolitePilot and LightSight Software) have accelerated metabolic profiling with a more automated approach to interpreting fragment spectra, speeding up metabolite identification, structural elucidation, and metabolic site assignment. 6 Correlation of metabolites across multiple samples is possible with batch modes, providing a way to pinpoint lot-to-lot anomalies or conduct time course studies. Sharing this data amongst multiple groups and providing rapid feedback to medicinal chemists is assisted through software linkages to laboratory information management software (LIMS) and global databases, easing data transfer between neighboring laboratories or far-flung research centers. 10 Additional software improvements have enhanced the relevancy of peaks identified as drug-related material with the development of novel algorithms that can effectively mine large data sets in a meaningful way and ease fragment interpretation. Data sets obtained through all-in-one approaches, such as SWATH Acquisition (Figure 3), form a comprehensive safety net, capturing both predicted and unpredicted metabolites, and allow for retroactive re-evaluation of the data in the event of an overlooked metabolite. But the enormity of the collection of all MS/MS data for a particular sample raises concerns on Real-time multiple mass defect (RT-MMDF) algorithm on the TripleTOF 5600 System Increased productivity by simultaneously capturing both qualitative and quantitative data via: Single injection workflows that capture both TOF MS and TOF MS/MS Increased MS/MS triggering efficiency on a UPLC time scale (2-3 sec. peak width) More accurate identification and confirmation of analytes in complex, in vivo samples (e.g., plasma with PEGs, bile samples, tissue samples) SWATH Acquisition for data-independent, all-in-one MS/MS fragmentation Comprehensive quantitative and qualitative analysis in a single injection captures MS/MS information for both predicted and unpredicted metabolites, creating the ultimate safety net Informative, more complete MS/MS spectra for better metabolite structure prediction and site-modification identification including: All-inclusive MS/MS for low-level metabolite/catabolite ID Retention of isotope pattern in MS/MS for each fragment ( 14 C/SIL ADC metabolism studies) Spectra are less complex than traditional DIA techniques and display more drug-related peaks. High-resolution quantification reduces the potential for interferences, yet maintains the sensitivity and dynamic range of leading triple quads. Selective, MRM-style quantitation using product ion mass and summation of product ions Easy method development and retrospective data mining Requirement for sample-specific method development is eliminated. Creation of a digital archive of all analytes enables post-acquisition investigation without additional injections. SelexION Technology for metabolite identification workflows Capable of separating isobaric metabolites or isobaric co-administered drugs Reduced chromatographic run times accelerate productivity Elimination of tedious LC method development and expensive columns reduce cost of analysis Selective and specific quantification provide accurate results for PK profiling and clearance rates. Enhanced structure elucidation capabilities achieved with more relevant, less complex MS/MS spectra Elimination of background noise and co-eluting interference Increased sensitivity lowers the LLOQ and boosts the S/N ratio Better peak integration facilitates improvement to data quality benchmarks (%CV, accuracy, dynamic range, LLOQ) Table 3: Key benefits of pivotal AB SCIEX metabolite screening workflows how to isolate drug-related material that is overshadowed by background noise. SWATH Acquisition employs an algorithm called principal components variable grouping (PCVG) analysis to filter unbiased data sets, highlighting peaks of interest while removing obfuscating background or chemical noise. The resulting spectra, overall, are less complex after PCVG-filtering and display more relevant drug-related peaks, which represent an inclusive record of all metabolites and their changes over time. 2,3 In that vein, the algorithm for real-time multiple mass defect filtering (RT-MMDF) performs a similar feat, but is employed during data-dependent acquisition. (Examples of RT-MMDF applied to metabolite discovery are provided. 8,9 ) During UHPLC/ MS acquisition, the RT-MMDF filter identifies peaks related to the parent drug, selectively triggering MS/MS scans to produce a trimmer data set that accentuates metabolite information without further post-acquisition processing (Figure 4). Both of these innovative approaches PCVG-filtering and RT-MMDF continue to effectively redefine and re-invigorate the solutions needed for the complex challenges of therapeutic drug development. 10 RUO-MKT-01-1583-A DRUG METABOLISM www.absciex.com

For Research Use Only. Not for use in diagnostic procedures. AB SCIEX TripleTOF 5600 System Accurate mass at the speed and sensitivity of a triple quadrupole: Fast MS and MS/MS acquisition speeds compatible with fast chromatography Resolution over 30,000 External mass accuracy ~1 ppm 4 orders of linear dynamic range MetabolitePilot Software Accurate mass data processing and interrogation: Generate cleaner, more relevant data with multiple mass defect filtering (MMDF) Store and retrieve critical information in the compound library and results database Quickly process multiple sample sets in batches Increase confidence in your results with intelligent scoring and easy-to-visualize color-coding Predict formulae with a high level of chemical intelligence AB SCIEX QTRAP 5500 System A single platform for drug metabolism and bioanalytical quantification workflows Targeted and non-targeted workflows Increased sensitivity Increased speed Full quantitative capabilities LightSight Software Exploit the full functionality of QTRAP Technology and processing strategies for multiple metabolite ID workflows pmrm high-sensitivity, targeted approach for really low-level detection and confirmation PI/NL (+/-ve polarity switching) structure-based filtering approach ideal for reactive metabolite screening Multiple ion monitoring and Q3 single MS strategies for a non-targeted approach INTRODUCTION Table 4: Simple, clear metabolism workflows for every stage of drug discovery. The TripleTOF 5600 System and the MetabolitePilot Software deliver high sensitivity quantification and new qualitative capabilities on a single platform. The QTRAP 5500 and LightSight Software use predictive MRM and multiple precursor ion and/or neutral loss survey scans in a single analysis, including polarity switching. Key features of AB SCIEX instrumentation for metabolite profiling As a global front-runner in the development of innovative technology, AB SCIEX continues to provide technological solutions that are transforming the drug pipeline. Each step of drug development has a unique set of benchmarks that must be met prior to advancement (Figure 2). Early in the discovery phase, rapid structural identification and in vitro metabolic stability results are used to screen thousands of compounds and drive the decision-making process surrounding compound optimization. Development phase requirements for integrated qual/quant workflows, quantitation of low-level reactive metabolites, and high-level structure elucidation must meet the compliance and validation standards of regulatory agencies and clinical safety. Mindful of the need for high productivity, AB SCIEX has advanced high-throughput workflows and remarkable, high-performance instruments that streamline the journey from the lab to the clinic for the development of modern, new therapeutics. Discussed below are the workflows and instruments that have set the industry s standards for competitive operation (summarized in Table 4). 1) High-resolution mass spectrometry quant/qual workflows: TripleTOF 5600 system and MetabolitePilot Software The superb quantitative capacity of a triple quadrupole and the high-performance accurate mass analyzer of a high-resolution time-of-flight mass spectrometer are united into one innovative, hybrid instrument, the TripleTOF 5600 system that performs both quantitative and qualitative analyses with just one method. Throughout every stage of the drug discovery process, it is essential to find, identify, and confirm metabolites as quickly as possible, and the workflows designed for the TripleTOF system enable the fast, accurate metabolic profiling necessary for both ADMET and DMPK studies. The collection of information-rich data using a specialized quant/qual workflow (Figure 5) and the selective extraction of drug-related information during data acquisition produce highly-relevant structural information for detection and characterization of metabolites in the same run. The TripleTOF system has the capacity to collect both MS and MS/MS scan data simultaneously; whilst survey scans generate TOF-MS spectra, a real-time filter based on mass defects (RT-MMDF) triggers MS/MS acquisition of peaks similar to the parent drug, rather than on unrelated background noise, so that only highly relevant information is exhibited in the resulting spectra. Another feature of the hybrid instrumentation is the combination of high resolution and high detector speed that can maintain sufficiently high resolution at low m/z, thereby including even more fragments in the equation for unambiguous structural assignment. (For a full listing of TripleTOF system features, see Table 5.) Altogether, intelligent data acquisition, simultaneous quant/qual analyses, and high scan speeds packaged in one hybrid instrument can support these powerful metabolite workflows. www.absciex.com DRUG METABOLISM 11

INTRODUCTION The concept of a real-time algorithm for multiple mass defect filtering Regarded as separate from data-processing algorithms Eliminates MS/MS-triggering on background noise Determines which ion(s) are significantly changing with time Selects the best ion(s) to target for MS/MS acquisition Applied during UPLC/MS acquisition Exists as part of information-dependent data acquisition (IDA) logic Table 5: Key features of TripleTOF System workflows for metabolite profiling Coupled with MetabolitePilot Software, the TripleTOF system can alleviate the data analysis bottlenecks traditionally associated with metabolite discovery by supplying one integrated platform for all metabolite profiling tasks, including metabolite identification, confirmation of metabolic site, time-course tracking, and inter-species metabolite comparisons. Offering one software package simplifies and automates the interpretation of complex, unwieldy data sets for the fast deconstruction of metabolite information that is crucial to compound optimization. The multifaceted quant/qual workflow has the flexibility and versatility to deliver metabolite information needed for each stage of drug development: Early drug discovery: High-throughput quant/qual assays can quickly analyze microsomal clearance data, matching fragments to proposed structures to determine critical soft spot information, as well as quantitating parent drug concentrations to establish metabolic stability all using a generic acquisition methodology in a single run. Late stage discovery: The wide-ranging approaches for data filtration (e.g., neutral loss, product ions, mass defect, isotope patterns) provide multiple methods for the discovery of expected and unexpected metabolites, even low-level, and phase II reactive metabolites. Accurate mass, automated structure-driven processing, and weighted scoring of structures speeds confidence in determining the site of metabolism. Automated correlation of peak areas with analog data delivers easier relative quantitation of metabolite and parent peaks for time course studies. Development stage: Processing of accurate mass and high resolution data, low range mass accuracy, and isotope patterns accelerate fragment assignment for definitive structural elucidation and correlation. Integrated processing of pharmacokinetic batch data using MultiQuant Software reveals metabolite concentrations and kinetic profiles relative to the parent, relying on the fast scanning speeds and the high sensitivity of the TripleTOF system to maintain resolution and to detect minor, low-level metabolites. 2) All-in-one, data-acquisition workflows for comprehensive metabolite profiling: SWATH Acquisition and the TripleTOF system SWATH Acquisition takes its name from the narrow precursor mass range (or swath) of ions selected in Q1 for advancement to Q2 for collision-induced fragmentation (Figure 3). Relying on the fast scan speed of the TripleTOF 5600 system to sequentially process a broad aggregate of narrow mass ranges in a small amount of time, SWATH Acquisition generates a comprehensive map of MS/MS spectra for every ion at every time point. Effectively simplified using PCVG-filtering and other post-acquisition data mining tools, these complex SWATH Acquisition data sets are transformed into spectra comprised of peaks relevant to the parent drug. In this way, one of the primary challenges of metabolite profiling is overcome the all-inclusive detection of metabolites, including unpredicted and trace-level, is accomplished just one injection without the need for specialized, sample-specific methods. Using data-independent methods, such as SWATH Acquisition, provides accessible results for more straightforward metabolite structure elucidation and identification. A layer of selectivity is established when using successive narrow mass windows to record MS/MS of the chosen ions within, converting complex MS e spectra to simplified datasets displaying relevant, drug-related peaks. The applicability of SWATH Acquisition data to high-resolution quantitation is extended when data is collected using a TripleTOF 5600 system. Using MRM-style methods to gather single product ion peak areas or to sum multiple product ions, metabolite quantitation can be accomplished with the same sensitivity and the dynamic ranges as those achieved on leading triple quadrupole mass spectrometers. High-resolution acquisition lends additional weight to the data quality by improving the completeness of MS/MS data sets for structural elucidation and permitting narrower mass windows to be used for peak selection, removing potentially confounding interferences from the quantitation process. The impact that the SWATH Acquisition workflow has on throughput and productivity during drug discovery and development is unparalleled. Access to an all-encompassing digital archive of complete MS/MS quantitative and qualitative information for every peak at every time point in a sample is valuable at every stage for increasing productivity, throughput, and data quality. (For a review of the additional features of SWATH Acquisition, see Table 6.) Unique qualitative features 1. Less complex MS/MS spectrum than traditional DIA techniques like MS e 2. Retention of isotope pattern for each fragment due to wider Q1 selection Supports 14 C/SIL ADC metabolism studies 3. Capture of 100% MS/MS for comprehensive identification of minor metabolites/catabolites Unique quantitative features 1. Selective MS/MS quantification realized using MRM-style methods for measurement of single product ions or for summing multiple product ions. 2. Multicomponent quantification is feasible with this single-acquisition method. Quantification of total mab, conjugated and free small molecule Table 6: Key features of SWATH Acquisition on the TripleTOF System for metabolite ID. 12 RUO-MKT-01-1583-A DRUG METABOLISM www.absciex.com

For Research Use Only. Not for use in diagnostic procedures. 3) Cutting-edge workflows for detecting low-level metabolites: QTRAP 6500 system and LightSight Software Another hybrid instrument, the QTRAP 6500 system, a unique combination of triple quadrupole and linear ion trap, delivers the enhanced speed and sensitivity needed for rapidly detecting the most number of metabolites using the minimum number of injections an efficient approach that complements accurate mass HRMS (see Table 4). During in vivo testing, drugs and metabolites need to be detected at physiologically relevant concentrations, which require an exceedingly sensitive approach. Additionally, early discovery of toxic, reactive metabolites is critical for the selection of appropriate candidates during drug development. Building on the significant gains in sensitivity and fast scanning speeds resulting from modern ion trap innovations, pmrm methods find and confirm the presence of ultra-low level in vivo and in vitro metabolites. These highly sensitive, targeted experiments are based on the parent drug fragmentation pattern and possible biotransformations to yield significantly more metabolites, even from complex biological matrices. Reactive metabolite screening experiments are also enabled by the high scan speeds of the QTRAP system. Dual scan surveys (precursor ion and neutral loss) can be conducted in one fast IDA cycle; and when combined with positive/negative polarity switching, these are the first workflows that can detect metabolites of varying polarities in the same run with sufficient sensitivity and accuracy for the highest level of confidence. In just a single injection, these highly sensitive workflows, pmrm and the dual scan IDA methods, provide the reassurance that an all-encompassing list of even the most minor of metabolites has been generated. Unique quant/qual characteristics 1. SelexION Technology drives quantitative workflows, but has the added benefit of boosting qualitative techniques. 2. Other mobility techniques are qual focused, not quant. a. SelexION technology s pre-ion-source location maintains precursor selection and fragmentation. b. Ion mobility spectrometry (IMS) occurs in the collision cell and provides no selectivity before Q1. c. FAIMS, another ion-mobility-based spectrometry, displays reduced robustness and reliability. 3. Chemical modifiers gives the flexibility to try various options for better separation. 4. SelexION technology is compatible with QTRAP and TripleTOF systems. a. MRM-style application for quantitation (continuous flux of ions) b. Full-scan QTOF (pulsed technique) 5. Easy switch between DMS on and off modes a. Allows ions to be transmitted in transparent mode with voltages turned off b. Easily installed in a few minutes without breaking vacuum or using any tools Table 7: Key features of SelexION Technology for metabolite identification. Early stage discovery: In combination with pmrm methods, polarity switching and fast scanning can quickly and sensitively detect low-level, transient metabolites, both predicted and unpredicted, in biological samples. The detection of minor, but potentially highly toxic, reactive metabolites by identifying glutathione-conjugates is very important for early safety assessments. Building these compound specific acquisition methods, acquiring the data, and then processing it can be consolidated on just one platform LightSight Software. Late stage discovery: Highly sensitive pmrm scans used during first-pass, in vitro and in vivo metabolic screening can quickly and efficiently profile metabolites at clinically relevant concentrations, while simultaneously generating quantitative data for metabolic stability and pharmacokinetic studies. The high sensitivity of the linear ion trap detects more trace metabolites and their fragments, which in turn provide more information for structural assignments. INTRODUCTION Figure 4: An illustration of multiple mass defect ranges that were calculated in MetabolitePilot Software. www.absciex.com DRUG METABOLISM 13