Thesis
Reference
Model-informed dose selection in early oncology drug development
XIONG, Wenyuan
Abstract
Drug developers in oncology indication has been struggling with a low probability of success for decades. In my thesis, we studied how to improve the clinical proof-of-concept success rate with quantitative dosing rationale. We reviewed the classical dose selection strategies and the emerging transition of mathematical model-informed optimization strategies in anti-cancer drug development. Then we presented two case studies: first-in-human dose selection of avelumab, a monoclonal antibody, and the recommended phase II dose determination of tepotinib, a small-molecule kinase inhibitor. It showed how quantitative tools informed the dose selection with precision, and streamlined the early drug development path by avoiding unnecessary dose steps. The accumulating clinical data also proved, in both cases, successful dose strategies. By integrating preclinical in-vitro/in-vivo and clinical data across multiple development stages, the quantitative frameworks interpret the accumulating knowledge with great synergy, showing a high potential of model-informed optimization strategy to improve efficiency in drug development.
XIONG, Wenyuan. Model-informed dose selection in early oncology drug development. Thèse de doctorat : Univ. Genève, 2019, no. Sc. 5436
DOI : 10.13097/archive-ouverte/unige:133935 URN : urn:nbn:ch:unige-1339355
Available at:
http://archive-ouverte.unige.ch/unige:133935
Disclaimer: layout of this document may differ from the published version.
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UNIVERSITÉ DE GENÈVE FACULTÉ DES SCIENCES
Département des Sciences Pharmaceutiques Professeur Chantal Csajka
MERCK SERONO
Merck Institute for Pharmacometrics Dr. Pascal Girard, HDR
MODEL-INFORMED DOSE SELECTION IN EARLY ONCOLOGY DRUG DEVELOPMENT
THÈSE
présentée à la Faculté des sciences de l’Université de Genève
pour obtenir le grade de Docteur ès sciences, mention sciences pharmaceutiques
par
Wenyuan Xiong de
Chine
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Scientific Communications
Poster Presentations
1. W.Xiong, H.Tian, A.Clark, J.Shaw, R.Kaleta, I.Celik, P.Girard, Pharmacodynamic (PD) Biomarkers for the p70S6K/Akt Inhibitor, M2698: Translation from Animal to Human and Relevance to Dose Selection, ESMO Annual Meeting 2017, Madrid, Spain
2. W.Xiong, B.Neuteboom, A.Munafo, H.Dolgos, A.Bernhardt, D.Zhang, P.Girard, Performance of target-mediated drug disposition (TMDD) modeling compared with allometric extrapolation for establishing the first-in-human avelumab dosage, ACOP 2017, Fort Lauderdale, USA
3. W.Xiong, S.El.Bawab, F.Bladt, M.Meyring, M.Klevesath, G.Falchook, D.S.Hong, A.Johne, P.Girard, Model-based phase II dose selection of the c-Met inhibitor tepotinib (MSC2156119J), AACR Annual Meeting 2015, Philadelphia, USA
4. W.Xiong, S.El.Bawab, F.Bladt, M.Meyring, M.Klevesath, G.Falchook, D.Hong, A.Johne, P.Girard, PK/PD Modeling of the c-Met Inhibitor MSC2156119J to Establish the Recommended Phase II Dose, 2014 PAGE meeting, Alicante, Spain
5. W.Xiong, A.Paoletti, A.Bernhardt, P.Girard, B.Testa, Pharmacokinetic interspecies extrapolation of a fully human mAb from animal to human, PAGE meeting 2012, Venice Italy
Oral Presentations
1. Human PK prediction of therapeutic monoclonal antibody: a case study of avelumab, 1st European Course on Pharmacokinetics and Pharmacodynamics of Protein Therapeutics: Principles and Pharmacometric Approaches, Lausanne, Switzerland, 2016
Publications
1. Falchook GS, Kurzrock R, Amin HM, Xiong W, Fu S, Piha-Paul SA, Janku F, Eskandari G, Catenacci DVT, Klevesath MB, Bruns R, Stammberger U, Johne A, Bladt F, Friese-Hamim M, Girard P, El Bawab S, Hong DS, First-in-Man Phase I Trial of the Selective MET Inhibitor Tepotinib in Patients with Advanced Solid Tumors, Clin Cancer Res. 2019 Dec 10
2. W.Xiong, P.Girard, H.Dolgos, D.Zhang and C.Csajka, Performance of Target-Mediated Drug Disposition Modeling Compared with Empirical Allometric Extrapolation to Establish First-in-Human Avelumab Dosage (targeted journal: Clin Pharmacokinet) 3. W.Xiong, F.Bladt, A.Johne, M.Klevesath, M.Friese-Hamim, G.Falchook G, D.Hong,
P.Girard, S.El.Bawab, Translational pharmacokinetic-pharmacodynamic modelling of preclinical and clinical data of oral c-Met inhibitor tepotinib to determine the Recommended Phase II Dose (targeted journal: Clin. Pharmacol. Ther)
4. W.Xiong, P.Girard, C.Csajka, Joint PK – tumor size mode to characterize the exposure – response relationship of avelumab in second-line NSCLC patients (targeted journal:
Clin. Pharmacol. Ther)
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TABLE OF CONTENTS
CHAPTER I GENERAL INTRODUCTION ... 12
I.1 Cancer Facts ... 12
I.2 Ancient History of Cancer Research ... 12
I.3 Modern Understanding of Cancer ... 13
I.4 Evolution of Cancer Treatment ... 14
I.5 Drug Development in Oncology ... 18
I.6 Objectives of the Thesis ... 20
I.7 Reference ... 20
I.8 Appendix ... 22
CHAPTER II DOSING RATIONALE OF NEW ANTICANCER THERAPY IN FIRST-IN-HUMAN TRIAL ... 28
II.1 First-in-human Trial ... 30
II.2 Starting Dose in Oncology First-In-Human Trial: Regulatory Perspectives ... 32
II.3 Efficiency of FIH Dose Scheme in Oncology Indications ... 33
II.4 Dose escalation and recommended phase II dose ... 35
II.5 Bayesian Approaches ... 36
II.6 Pharmacokinetic / Pharmacodynamic Modeling to Inform FIH Dose Decision ... 37
II.7 A schematic Approach of FIH Dose Selection ... 38
II.8 References ... 38
CHAPTER III CLINICAL DOSAGE ESTABLISHMENT OF AVELUMAB, A BROAD SPECTRUM ANTICANCER MONOCLONAL ANTIBODY ... 42
III.1 Performance of Target-Mediated Drug Disposition Modeling Compared with Empirical Allometric Extrapolation to Establish First-in-Human Avelumab Dosage ... 44
III.1.1 Abstract ... 46
III.1.2 Introduction ... 47
III.1.3 Methods ... 49
In vivo studies ... 49
Internalization studies ... 51
Bioanalytical assays ... 51
Data analysis ... 52
III.1.4 Results ... 59
MM approximation model ... 59
Human PK extrapolation and external validation ... 63
III.1.5 Discussion ... 65
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III.1.6 Study Highlights ... 68
III.1.7 Acknowledgements ... 69
III.1.8 References ... 69
III.1.9 Appendix ... 71
III.2 Joint PK – Tumor Size Mode to Characterize the Exposure – Response Relationship of Avelumab in NSCLC Patients ... 72
III.2.1 Non-small-cell Lung Cancer ... 72
III.2.2 Surrogate Efficacy Endpoint of Tumor Dynamics ... 73
III.2.3 Patients and Methods ... 74
Trial patients ... 74
Graphical analysis of tumor growth inhibition ... 76
Mathematical model describing tumor growth inhibition (TGI) ... 76
Cross interaction between pharmacokinetics and tumor dynamics ... 78
Computation method and model qualification ... 79
III.2.4 Results ... 80
Description of patient characteristics ... 80
Graphical analyses ... 81
Tumor growth inhibition model ... 83
Joint model of pharmacokinetics and tumor growth inhibition ... 84
Model qualification ... 87
III.2.5 Discussion ... 90
III.2.6 Reference ... 92
III.2.7 Appendix ... 94
CHAPTER IV RECOMMENDED PHASE II DOSE SELECTION OF TEPOTINIB, A SMALL MOLECULE TYROSINE KINASE INHIBITOR 106
IV.1 Translational Pharmacokinetic - Pharmacodynamic Modelling of Preclinical and Clinical Data of Oral c-Met Inhibitor Tepotinib to Determine the Recommended Phase II Dose ... 107IV.1.1 Abstract ... 108
IV.1.2 Introduction ... 109
IV.1.3 Materials and Methods ... 111
Work frame overview ... 111
Tepotinib ... 112
MSC2571109... 112
Preclinical and clinical studies ... 113
Modelling of preclinical data ... 117
Modelling of human data... 118
Modeling software ... 121
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IV.1.4 Results ... 122
Modelling of preclinical data ... 122
Modelling of human data... 127
Simulation of population target inhibition–time profiles and clinical dose selection ... 130
Efficacy contribution of MSC2571109, the major human circulating metabolite ... 131
IV.1.5 Discussion ... 132
IV.1.6 References ... 135
CHAPTER V CONCLUSIONS AND PERSPECTIVES ... 139
V.1 Conclusions ... 139
V.2 Perspectives ... 141
Better Understanding of Dynamics in the Tissue ... 141
Preclinical Dose Finding Studies in Patient-Derived Xenograft Model ... 142
V.3 References ... 143
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LIST OF FIGURES
Figure I.5.1 Likelihood of approval from phase I drug development ... 18
Figure III.1.1 Schematic representation of avelumab activity against tumor cells via PD-L1 inhibition and ADCC ... 48
Figure III.1.2 Diagram of MM approximation PK model ... 54
Figure III.1.3 Diagram of TMDD model ... 56
Figure III.1.4 pcVPC plots of avelumab plasma concentrations in mice and monkeys ... 60
Figure III.1.5 pcVPC plots of avelumab PK profiles in mice and monkeys and PBMC TO in mice Figure III.1.6 62 External validation of preclinical model–based human PK extrapolation ... 64
Figure III.2.1 Spider plot of longitudinal tumor size stratified by quartiers of AUC at steady state. Figure III.2.2 82 Spider plot of longitudinal tumor size stratified by quartiers of Ctrough at steady state. Figure III.2.3 83 Spaghetti plots of observed and model-predicted tumor size profiles. ... 87
Figure III.2.4 Basic goodness-of-fit plots for joint PK – tumor size model ... 88
Figure III.2.5 Individual plot of observation versus prediction (subject ID=10135) ... 88
Figure III.2.6 Visual predictive check of the pharmacokinetic and tumor size models. ... 89
Figure III.2.7 Spider plots of longitudinal tumor size: stratified by positive or negative PD-L1 expression in the tumor cell at baseline. ... 94
Figure III.2.8 Spider plots of longitudinal tumor size: percentage of tumor cells with PD-L1 H- score ≥1 in at baseline. ... 95
Figure III.2.9 Spider plots of longitudinal tumor size: percentage of tumor cells with PD-L1 H- score ≥2 in at baseline. ... 96
Figure III.2.10 Spider plots of longitudinal tumor size: stratified by positive or negative tumor infiltration lymphocytes at baseline. ... 97
Figure III.2.11 Spider plots of longitudinal tumor size: stratified by smoking status. ... 98
Figure III.2.12 Spider plots of longitudinal tumor size: stratified by histology. ... 99
Figure III.2.13 Spider plots of longitudinal tumor size: stratified by EGFR mutation status. ... 100
Figure III.2.14 Spider plots of longitudinal tumor size: stratified by ALK mutation status. ... 101
Figure III.2.15 Spider plots of longitudinal tumor size: tumor burden at baseline. ... 102
Figure III.2.16 Spider plots of longitudinal tumor size: number of prior anti-cancer therapies. ... 103
Figure III.2.17 Individual plots of tumor size observation versus prediction ... 104
Figure IV.1.1 Workflow of model development. ... 111
Figure IV.1.2 Data exclusion tree for clinical PD analysis set... 120
Figure IV.1.3 Hysteresis curves: phospho c-Met vs. plasma and tumor concentration after oral administration of tepotinib 5, 15, 50, and 200 mg/kg. ... 123
Figure IV.1.4 Turnover model for phospho-c-Met inhibition ... 123
Figure IV.1.5 Tumor growth inhibition in the efficacy studies of tepotinib (A) and MSC2571109A (B) ... 125
Figure IV.1.6A Sustained high level of phospho-c-Met inhibition is required for efficacy. ... 126
Figure IV.1.7 Phospho-c-Met inhibition in tumor biopsies compared with baseline after repeated dosing for at least 9 days. ... 130
Figure IV.1.8 Simulation of dose-dependent phospho-c-Met (relative to baseline) time profile in humans. ... 131
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LIST OF TABLES
Table I.8.1 List of FDA Approved Targeted Therapy for Hematological Cancer: dated 31.08.2018 .
... 22
Table I.8.2 List of FDA Approved Targeted Therapy for Solid Tumor Cancer: dated 31.08.2018 24 Table II.2.1 List of Approaches in Selecting Starting Dose in First-in-Human Trial ... 33
Table III.1.1 Summary of in vivo studies of avelumab ... 50
Table III.1.2 Parameter estimates of the TMDD model. ... 61
Table III.1.3 Comparison of model-based 90% prediction interval and FIH observation. ... 65
Table III.2.1 Summary of Joint PK – Tumor Size Model Analysis Set ... 81
Table III.2.2 Parameter estimates of joint PK – tumor size inhibition model ... 85
Table IV.1.1. PK/PD parameters of phospho-c-Met inhibition in KP-4 cell line xenograft tumors ... 123
Table IV.1.2 Parameter estimates for the Simeoni TGI model in KP-4 xenograft mice... 124
Table IV.1.3 Population PK parameter estimates of joint tepotinib and MSC2571109 model in humans ... 128
Table IV.1.4 PK/PD parameters of phospho-c-Met inhibition in human tumors ... 129
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ABBREVIATIONS
AB Antigen binding
ACC Adrenocortical carcinoma ADC Antibody-drug conjugate
ADCC Antibody-dependent cell-mediated cytotoxicity ADME Absorption, disposition, metabolism and elimination AIC Akaike information criterion
ALK Anaplastic lymphoma kinase AUCss Area under the curve at steady state CI Confidence interval
CL Clearance
CLX Cell line xenograft
CPRC Castrate-resistant prostate cancer CRC Colorectal cancer
CRM Continual reassessment method
CT Computed tomography
CTLA-4 T-lymphocyte-associate antigen 4 Ctrough_SS Trough concentration at steady state DAR Drug-antibody ratio
DoR Duration of survival
DV Dependent value
EC50 drug concentration corresponding to the half maximum effect ECOG Eastern Cooperative Oncology Group
EGFR Epidermal growth factor receptor Emax Maximum treatment effect
EoW Every other week E-R Exposure - Reponses
FACS Fluorescence-activated cell sorter Fc Fragment crystallizable
FcRn Neonatal Fc receptor
FDA Food and drug administration
FIH First-in-human
FOCEI First-order conditional estimation with interaction GCP Good clinical practice
GEJ Gastroesophageal junction HGF Hepatocyte growth factor
HHD Highest human dose
HNSCC Head and neck squamous cell carcinoma HNSTD Highest non-severely toxic dose
HPLC High performance liquid chromatography
ICH International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use
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IgG Immunoglobulin G
IND Investigational new drug IPRED Individual prediction
IV Intravenous
IWRES Individual weighted residual
KG Tumor growth rate
Km Michaelis-Menten constant KS Tumor shrinkage rate
λ Exponential decrease constant of tumor shrinkage rate LLD lower limit of detection
mAB Monoclonal antibody
MABEL Minimum anticipated biological effect level MBC Metastatic breast cancer
MFI measured florescence intensity MHC Major histocompatibility complex
MM Michaelis-Menten
MRI Magnetic resonance imaging MTD Maximum tolerated dose NCA Non-compartmental analysis NOAEL No observed adverse effect level NONMEM Nonlinear mixed effect modeling NSCLC Non-small cell lung cancer OBD Optimal biologic dose ORR Objective response rate
OS Overall Survival
PAD Pharmacological active dose PBMC Peripheral blood mononuclear cell
pcVPC Prediction-corrected visual predictive checking
PD Pharmacodynamics
PD-1 Programmed death protein 1 PD-L1 Programmed death-ligand 1 PDX Patient-derived xenograft PFS Progression free survival PI Prediction interval
PK Pharmacokinetics
pMET Phosphorylated c-Met PoC Proof-of-concept POS Probability of success PRED Population prediction
PsN Perl-speaks-NONMEM
Q Intercompartmental clearance
RECIST Response Evaluation Criteria in Solid tumor
RO Receptor occupancy
10 RP2D Recommended phase II dose RSE Relative standard error RTK Receptor tyrosine kinase
SHR Shrinkage
STD10 The severely toxic dose in 10% of the animals TGI Tumor growth inhibition
TKI tyrosine kinase inhibitor
TMDD Target-mediated drug disposition
TO Target occupancy
TS Tumor Size
UC Urothelial carcinoma
V1 Centrale volumes of distribution V2 Peripheral volumes of distribution Vmax Maximum nonlinear elimination rate VPC Visual predictive checking
γ Sigmoid exponent of Emax model ΔOFV Change of objective function value
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CHAPTER I
GENERAL INTRODUCTION
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CHAPTER I GENERAL INTRODUCTION
I.1Cancer Facts
Cancer is a generic term for a collection of diseases that affect various parts of the human body, characterized by abnormal cell growth beyond its normal cycle and boundary. In comparison to healthy cells, cancer cells are less differentiated and invasive to their surrounding tissues.
Also, as cancer cells grow, they could break off from the stroma and travel to distant organs through blood or lymph flow, and form new lesions known as tumor metastasis.
Cancer is a major public health concern, and the second leading cause of morbidity and mortality worldwide: it counts for 8.8 million deaths in 2015. Almost 1 in 6 deaths is due to cancer [1]. In the year of 1971, US President Richard Nixon signed the National Cancer Act, which was seen as declaring “war on cancer”. The national initiative of significant investment in cancer research and therapeutic oncology jointly by the government, academia, and drug industry was rewarding: in the United States, the cancer death rate dropped continuously by a total of 26% from 1991 to 2015 [2].
I.2 Ancient History of Cancer Research
The terminology of cancer is believed credited to Greek physician Hippocrates (460 - 370B.C.), who used its Greek cognate “karkinos” to describe carcinoma tumors [3]. This word means crab literally in Greek, and the spreading invasion of a solid malignant tumor to the surrounding tissues resembles the appearance of crab’s legs. The earliest known documentation of cancer, however, dates back to seventeenth-century B.C. in an ancient Egyptian medical textbook Edwin Smith Papyrus, which is believed an incomplete copy of even older manuscript dated around 3000 B.C.. In this Pyramid Age trauma surgery manuscript, several forms of breast
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cancer was described treated with cauterization [4]. The oldest known case of cancer in humans was found in the skeleton of a Scythian king (southern Siberia, Russia) living in the Iron Age about 700 B.C.. Metastatic prostate cancer was diagnosed based on morphological and biochemical investigation of the skeleton [5].
After the sixteenth century, experimental investigation began to be widely adopted in medical research. Italian pathologist Giovanni Morgagni published his work (De sedibus, et causis morborum per anatomen indagatis, 1761) of introducing pathological autopsy as a scientific method to relate the diseases of the patient to the pathological findings post-mortem, which laid the foundation for oncology research. In the nineteenth century, German pathologist Rudolf Virchow, the founder of cellular pathology, developed a systematic method of microscopic examination of the diseased tissue, setting the cornerstone of modern tumor pathology.
I.3 Modern Understanding of Cancer
The discovery of DNA double helix by Francis Cricks and James Watson in 1953 marked a milestone in the history of modern life science, opening an era of molecular biology. Through more than sixty year’s research on how genes control the chemical processes in the cell and regulate protein synthesis, substantial amount of knowledge on cancer has been accumulated at the molecular level.
Hanahan and Weinberg published a seminal review of cancer biology in 2000 [6], with further conceptual updates one decade later using information from transgenic animals and emerging biochemical assays [7]. The author distilled the daunting complexity of human cancer into eight hallmarks of acquired capabilities that are necessary for the development of neoplastic disease:
(1) sustaining proliferative signaling, (2) evading growth suppressors, (3) resisting cell death, (4) enabling replicative immortality, (5) inducing angiogenesis, (6) activating invasion and metastasis, (7) reprogramming of energy metabolism, (8) evading immune destruction. The
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acquisition of these hallmark traits is expedited by tumor-promoting inflammation and the underlying genome instability and mutation, with a further contribution of the tumor microenvironment created by a repertoire of recruited, ostensibly normal cells [7].
Among the different opinion holders, not only whether certain hallmarks are to be included is debatable [8], but what defines a cancer hallmark is also challenged: most of the listed traits are not distinguishing features of malignant tumors over benign tumors [9]. Sonnenschein et [10]
argued that cancer was a tissue-based disease, thus criticized the value of the hallmarks when considering cancer as a cell-based genetic disease. Despite the criticism, the rationalized organization of cancer cell characteristics provided insight into identifying potential therapeutic target.
I.4 Evolution of Cancer Treatment
Tumors have been treated with surgeries since ancient Greece, despite of knowing that malignant tumor was usually incurable and would come back after surgical intervention. During the following millennium of development in medical science, cancer treatment progressed little.
The surgical treatment then was primitive and prone to complication, including blood loss and severe infection. It might do more harm to patients than having no intervention at all. Until the nineteenth century, the general surgery achieved significant advancement after the invention of surgical anesthesia and the adoption of surgical sterilization. The technics of cancer operation designed to remove the entire tumor together with surrounding lymph nodes progressed rapidly in the following hundred years. Surgeons, later on, developed technical precision of minimizing the health tissues sacrificed during cancer operation, and preserving as much function as possible. The evolution of radical mastectomy to modified radical mastectomy and lumpectomy in treating breast cancer is a good example.
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Radiotherapy against solid tumors was introduced at the end of the nineteenth century, shortly after the discovery of X-ray by Wilhelm Röntgen in 1895 [11]. It uses high energy of ionizing radiation to damage the DNA of cancer cells and consequently to promote cell death. Different forms of radiation have been developed to deliver a higher level of energy. Computer technology assisted the precision of radiation with shaped radiation beams that target the tumor- invaded tissue and spare the healthy tissue.
During the same period of occurrence of radiotherapy, the “seed-and-soil” theory was raised by an English surgeon Stephen Paget, proposing that cancer cells could spread to other organs but not coincidentally, as seeds “only live and grow if they fall on congenial soil” [12]. This hypothesis was supported by evidence generated by modern cellular and molecular biology.
The understanding of tumor metastasis led to the growing awareness of the limitations of surgical intervention and radiotherapy, which dominated the field of therapeutic oncology until World War II (WWII), when systemic treatment was introduced in the clinic to treat leukemia and metastatic tumor.
The application of chemotherapy in cancer indication was discovered by coincidence. During WWII, sulfur mustard, a vesicant chemical warfare agent, was found to correlate with bone marrow and lymph node depletion in accidentally exposed troops [13]. Such lymph suppression effect was later tested in lymphoma patients in 1943. Marked tumor regression was observed, but the response was unfortunately incomplete and brief. Great excitement and expectation of potential cancer cure by chemical compound was triggered. Thousands of related alkylating agents were then tested in the following decades, including cyclophosphamide, which is widely used today for treating cancer and autoimmune diseases [14]. Other incidental discovery of anticancer agents includes methotrexate, a folic acid analog from a nutritional research of folic acid, and actinomycin D, which was found during an extensive scale screening of antibiotics
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from fermentation products [15]. The first broad-spectrum chemotherapy designed to treat non- hematologic malignancy was 5-fluorouracil (5-FU) in 1958. It targets a unique metabolic property of cancer cells: prominent uptake of uracil. In the middle of the 1950s, NCI oncologist Min Chiu Li pioneered the clinical application of surrogate tumor marker monitoring during his experimental treatment of metastatic choriocarcinoma with methotrexate, achieving complete remission in patients continuing chemotherapy until blood human chorionic gonadotropin (hCG) recovered to average level [16].
The birth of targeted therapy is linked to the Special Virus Cancer Program in the 1960s. It failed to identify any cancer-related virus, but morphed into a molecular biology research program [15] that succeeded in identifying oncogenes and developmental biology signaling pathways [17]. Distinguished from cytotoxic chemotherapy, most targeted therapies are cytostatic. They are designed to act selectively on specific enzymes or receptors, by either blocking or promoting the function of the targets that are relevant with cancer cell growth or evasion. In such a way of acting precisely against tumor cells, the collateral damage is minimized.
Different strategies and therapeutic modalities of target engagement have been applied in cancer therapy, including monoclonal antibodies that modulate the cell surface receptors or circulating soluble receptors, tyrosine kinase inhibitors that interact with oncogenic intracellular signal cascading pathways, hormone therapies that suppress the growth of hormone-sensitive tumors, angiogenesis inhibitors that interact with tumor vasculature or hypoxia in the micro- environment, antibody-drug conjugates that increase the precision of cytotoxic agent delivery, and immunotherapies that modulate the interaction between tumor cells and immune cells [18].
Immunotherapy has achieved groundbreaking clinical success in the recent years. It applies a unique logic to destroy cancer cells: instead of using xenobiotics to suppress cancer cell growth
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or promote cell death, immunotherapy boosts the human body’s natural defense system to fight against cancer cells. The idea of treating cancer with immunotherapy rooted back to the theory of cancer immunosurveillance by Thomas and Burnet in the 1950s [19]. The first clinical approval of immuno-oncology therapeutic modality is T-cell growth factor interleukin-2 (IL-2) for metastatic kidney cancer in 1991, followed by cancer vaccine sipuleucel-T for castration- resistant prostate cancer in 2010.
Long-awaited clinical efficacy of immunotherapy remained elusive in major cancer indications until the first launch of checkpoint inhibitor in 2011. A collection of monoclonal antibody checkpoint inhibitors that target programmed death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) axis and cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) now have shown promising efficacy of sustained anti-tumor response or even disease remission. These antibodies act on the interaction between the immune system and tumor cells, by either aiding the immune recognition and promoting immune response towards cancer cells, or relieving suppression of antitumor immunity. Accumulating clinical evidences suggest high potency of checkpoint inhibitor as a broad-spectrum therapeutic option in various locally advanced or metastatic cancer indications. Such leap forward of therapeutic outcomes has been widely acknowledged, including the 2018 Nobel Prize in Physiology or Medicine that was awarded to James Allison and Tasuku Honjo for their pioneer work in CTLA-4 and PD-1.
Since the launch of the first cancer targeted therapy of Tamoxifen for treating estrogen-positive breast cancer in 1962, the modern therapeutic oncology field has been primarily dominated by targeted therapies. The complete list of FDA-approved targeted therapies is summarized in Table I.8.1 and Table I.8.2.
18 I.5 Drug Development in Oncology
Statistics from FDA show that about 40% of the drugs in development is in oncology indication, and almost 50% of the breaking through designation is rewarded to anti-cancer drug candidates, despite having the lowest probability of success [20]. The likelihood of approval for phase I drug candidates in oncology is about 5%, while in all indications is close to 10% (Figure I.5.1).
Figure I.5.1 Likelihood of approval from phase I drug development
A reprint from A.Mullard, Parsing clinical success rates [21].
Drug development in oncology has its peculiarity. In contrast to the non-oncology first-in- human (FIH) trial, which is conducted in healthy volunteers, oncology FIH trial often recruits cancer patients with advanced-stage diseases, mostly in mixed solid tumor indications. Safety and tolerability are the primary endpoints of the oncology FIH trials, while pharmacokinetics, pharmacodynamics, and efficacy serve as secondary endpoints, which are highly variable due to the heterogeneity of the patient population. A newly emerged basket study presents an alternative approach in phase Ib/II development. The efficacy is assessed in a population harboring specific genetic mutations instead of with the same tumor origin. The trial population trends to be highly heterogeneous in terms of tumor type and disease characteristics, resulting in considerable variability of trial endpoints.
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Generally, two categories of efficacy endpoints are used in oncology studies: patient-centered endpoints, including overall survival (OS) and quality of life; and tumor-centered endpoints such as progression free survival (PFS), objective response rate (ORR) and duration of response (DoR) [22]. OS is considered the “golden standard”, which however, leads to lengthy duration of study, and further complexity to interpret the treatment effect among many other cofactors.
Due to the time and cost constraint, the phase II program usually does not accommodate a proper dosing finding study, in which multiple dose levels are tested, and exposure-responsese relationship is studied. In most cases, only one dose level is tested in the proof-of-concept study, referred to the recommended phase II dose (RP2D). A suboptimal dose in the pivotal study contributes to the hazard of late-phase failure. When targeting a niched population with specific genomic mutation or molecular profile, low prevalence of the disease also set the limit of the sample size in oncology study.
Using an automated algorithm to analyze the trial outcome of 21143 compounds, the clinical probability of success (POS) in drug development between 2000 and 2015 was reviewed [23].
Oncology trial was consistently found with the lowest POS among other indications, reaching the bottom level of 1.7% in 2012 and an uptick afterward that may relate to the recent break- through in immuno-oncology. Two critical factors were found in raising the overall clinical POS: implementation of a bio-marker driven strategy in order to select the right target population, and a successful phase II to phase III transition to establish clinical proof-of-concept (PoC) and optimal therapeutic dose range [24].
20 I.6 Objectives of the Thesis
In my thesis, we tackled the second factor: to improve the clinical PoC success rate with a quantitative dosing rationale that integrates data and knowledge across different development stages. The objectives are as follows:
• To summarize the latest regulatory guidelines for dose selection of FIH trials in oncology, and to review the classical dosing strategy and the state-of-art techniques of model-informed FIH dose prediction (Chapter II).
• To develop a semi-mechanistic translational modeling approach in predicting human exposure and FIH dose for avelumab, a monoclonal antibody targeting program death ligand-1 receptor (Chapter III.1).
• To learn-and-confirm the dosing rationale of avelumab based on phase Ib exposure- responsese analysis, a joint PK-tumor size model that takes into account the mutual interaction between pharmacokinetics and pharmacodynamics (Chapter III.2).
• To elaborate on the translational modeling approach with the integration of preclinical and FIH clinical data to inform the phase II dose selection for tepotinib, a small molecule tyrosine kinase inhibitor of the c-Met receptor (Chapter IV).
I.7Reference
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13. Marshall, E.K., Jr., Historical Perspectives in Chemotherapy. Adv Chemother, 1964.
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14. Emadi, A., R.J. Jones, and R.A. Brodsky, Cyclophosphamide and cancer: golden anniversary. Nat Rev Clin Oncol, 2009. 6(11): p. 638-47.
15. DeVita, V.T., Jr. and E. Chu, A history of cancer chemotherapy. Cancer Res, 2008.
68(21): p. 8643-53.
16. Hertz, R., M.C. Li, and D.B. Spencer, Effect of methotrexate therapy upon choriocarcinoma and chorioadenoma. Proc Soc Exp Biol Med, 1956. 93(2): p. 361-6.
17. Fischinger, P.J. and V.T. DeVita, Jr., Governance of science at the National Cancer Institute: perceptions and opportunities in oncogene research. Cancer Res, 1984.
44(10): p. 4693-6.
18. Tolcher, A.W., Antibody drug conjugates: lessons from 20 years of clinical experience.
Ann Oncol, 2016. 27(12): p. 2168-2172.
19. Dunn, G.P., et al., Cancer immunoediting: from immunosurveillance to tumor escape.
Nat Immunol, 2002. 3(11): p. 991-8.
20. Thomas, D., et al., Clinical Development Success Rates 2006-2015.
https://www.bio.org/sites/default/files/Clinical%20Development%20Success%20Rate s%202006-2015%20-%20BIO,%20Biomedtracker,%20Amplion%202016.pdf, 2016.
21. Mullard, A., Parsing clinical success rates. Nat Rev Drug Discov, 2016. 15(7): p. 447.
22. Fiteni, F., et al., Endpoints in cancer clinical trials. J Visc Surg, 2014. 151(1): p. 17-22.
23. Wong, C.H., K.W. Siah, and A.W. Lo, Estimation of clinical trial success rates and related parameters. Biostatistics, 2018.
24. Patel, D.D., et al., Phase 2 to phase 3 clinical trial transitions: Reasons for success and failure in immunologic diseases. J Allergy Clin Immunol, 2017. 140(3): p. 685-687.
22 I.8Appendix
Table I.8.1 List of FDA Approved Targeted Therapy for Hematological Cancer: dated 31.08.2018
Indications Small molecules Antibodies / Fusion Proteins Cell therapy Leukemia tretinoin (Vesanoid®),
imatinib mesylate (Gleevec®), dasatinib
(Sprycel®), nilotinib (Tasigna®), bosutinib (Bosulif®), ibrutinib (Imbruvica®), idelalisib (Zydelig®), venetoclax (Venclexta™), ponatinib hydrochloride (Iclusig®), midostaurin (Rydapt®),
enasidenib mesylate (Idhifa®), ivosidenib
(Tibsovo®)
alemtuzumab (Campath®), ofatumumab (Arzerra®), obinutuzumab (Gazyva®), blinatumomab (Blincyto®), inotuzumab ozogamicin (Besponsa®), gemtuzumab ozogamicin (Mylotarg™), rituximab (Rituxan®), rituximab and hyaluronidase human (Rituxan Hycela™)
tisagenlecle ucel
(Kymriah®)
Lymphoma vorinostat (Zolinza®), romidepsin (Istodax®), bexarotene (Targretin®), bortezomib (Velcade®), pralatrexate
(Folotyn®), ibrutinib
(Imbruvica®), siltuximab (Sylvant®), idelalisib (Zydelig®), belinostat (Beleodaq®),copanlisib hydrochloride
(Aliqopa™),acalabrutinib (Calquence®), , venetoclax (Venclexta™),
mogamulizumab-kpkc (Poteligeo®)
ibritumomab tiuxetan (Zevalin®), denileukin
diftitox (Ontak®),
brentuximab vedotin (Adcetris®), rituximab
(Rituxan®),obinutuzumab (Gazyva®), nivolumab (Opdivo®), pembrolizumab (Keytruda®), rituximab and hyaluronidase human (Rituxan Hycela™)
tisagenlecle ucel
(Kymriah®) ,axicabtage ne ciloleucel (Yescarta™
)
23 Multiple
myeloma daratumumab
(Darzalex™),elotuzumab (Empliciti™)
bortezomib (Velcade®), carfilzomib
(Kyprolis®), panobinostat (Farydak®), ixazomib citrate (Ninlaro®)
Myelodysplasti c/
myeloprolifera tive disorders
Imatinib mesylate (Gleevec®), ruxolitinib
phosphate (Jakafi®)
24
Table I.8.2 List of FDA Approved Targeted Therapy for Solid Tumor Cancer: dated 31.08.2018
Indications Small molecules Antibodies / Fusion Proteins Adenocarcinoma of
the stomach or gastroesophageal
junction
trastuzumab (Herceptin®), ramucirumab (Cyramza®)
Bladder cancer atezolizumab (Tecentriq™),
nivolumab (Opdivo®), durvalumab (Imfinzi™), avelumab
(Bavencio®), pembrolizumab (Keytruda®)
Brain cancer everolimus (Afinitor®) bevacizumab (Avastin®) Breast cancer everolimus (Afinitor®), tamoxifen
(Nolvadex), toremifene (Fareston®), fulvestrant (Faslodex®), anastrozole (Arimidex®), exemestane (Aromasin®), lapatinib (Tykerb®), letrozole (Femara®), palbociclib (Ibrance®), ribociclib (Kisqali®), neratinib maleate (Nerlynx™), abemaciclib (Verzenio™), olaparib (Lynparza™)
trastuzumab (Herceptin®), pertuzumab (Perjeta®), ado-
trastuzumab emtansine (Kadcyla®),
Cervical cancer bevacizumab (Avastin®),
pembrolizumab (Keytruda®) Colorectal cancer ziv-aflibercept (Zaltrap®),
regorafenib (Stivarga®) cetuximab (Erbitux®), panitumumab (Vectibix®), bevacizumab (Avastin®), ramucirumab (Cyramza®), nivolumab (Opdivo®), ipilimumab (Yervoy®)
Dermatofibrosarcoma
protuberans imatinib mesylate (Gleevec®) Endocrine/
neuroendocrine tumors
lanreotide acetate (Somatuline®
Depot), lutetium Lu 177-dotatate (Lutathera®), iobenguane I 131 (Azedra®)
avelumab (Bavencio®)
Head and neck cancer cetuximab
(Erbitux®), pembrolizumab (Keytruda®), nivolumab (Opdivo®)
25 Gastrointestinal
stromal tumor imatinib mesylate (Gleevec®), sunitinib (Sutent®), regorafenib (Stivarga®)
denosumab (Xgeva®)
Giant cell tumor of
the bone denosumab (Xgeva®)
Kidney cancer sorafenib (Nexavar®), sunitinib (Sutent®), pazopanib (Votrient®), temsirolimus (Torisel®), everolimus (Afinitor®), axitinib (Inlyta®), cabozantinib (Cabometyx™), lenvatinib mesylate (Lenvima®),
bevacizumab (Avastin®), nivolumab
(Opdivo®), ipilimumab (Yervoy®)
Liver cancer sorafenib (Nexavar®), regorafenib (Stivarga®), lenvatinib mesylate (Lenvima®)
nivolumab (Opdivo®)
Lung cancer crizotinib (Xalkori®), erlotinib (Tarceva®), gefitinib (Iressa®), afatinib dimaleate (Gilotrif®), ceritinib (LDK378/Zykadia™), osimertinib (Tagrisso™), alectinib (Alecensa®), brigatinib (Alunbrig™), trametinib (Mekinist®), dabrafenib (Tafinlar®)
bevacizumab (Avastin®), ramucirumab (Cyramza®), nivolumab (Opdivo®), pembrolizumab (Keytruda®), necitumumab (Portrazza™), atezolizumab (Tecentriq™), durvalumab (Imfinzi™)
Microsatellite
instability-high or mismatch repair- deficient solid tumors
pembrolizumab (Keytruda®)
Neuroblastoma dinutuximab (Unituxin™)
Ovarian
epithelial/fallopian tube/primary peritoneal cancers
olaparib (Lynparza™), rucaparib camsylate (Rubraca™), niraparib tosylate monohydrate (Zejula™)
bevacizumab (Avastin®)
Pancreatic cancer erlotinib (Tarceva®), everolimus (Afinitor®), sunitinib (Sutent®) Prostate cancer cabazitaxel (Jevtana®),
enzalutamide (Xtandi®), abiraterone acetate (Zytiga®), radium 223 dichloride (Xofigo®), apalutamide (Erleada™)
sipuleucel-T (Provenge®)
26
Skin cancer vismodegib (Erivedge®), sonidegib (Odomzo®), vemurafenib (Zelboraf®), trametinib (Mekinist®), dabrafenib (Tafinlar®), cobimetinib (Cotellic™), alitretinoin (Panretin®), encorafenib (Braftovi™), binimetinib (Mektovi®)
ipilimumab (Yervoy®), pembrolizumab (Keytruda®), nivolumab (Opdivo®), avelumab (Bavencio®),
Soft tissue sarcoma Pazopanib (Votrient®),
alitretinoin (Panretin®) olaratumab (Lartruvo™)
Stomach cancer pembrolizumab (Keytruda®)
Systemic
mastocytosis imatinib mesylate (Gleevec®), midostaurin (Rydapt®)
Thyroid cancer cabozantinib (Cometriq®), vandetanib (Caprelsa®), sorafenib (Nexavar®), lenvatinib mesylate (Lenvima®), trametinib
(Mekinist®), dabrafenib (Tafinlar®)
27
CHAPTER II
DOSING RATIONALE OF NEW ANTICANCER THERAPY IN FIRST-IN-HUMAN TRIAL
28
CHAPTER II DOSING RATIONALE OF NEW ANTICANCER THERAPY IN FIRST-IN-HUMAN TRIAL
Dose and dose-rate matter! [25]
29
Reprint from Klaassen, Curtis D., Louis J Casarett, and John Doull. Casarett and Doull's Toxicology:
The Basic Science of Poisons. 8th ed. New York: McGraw-Hill Education / Medical, 2013.
30 II.1First-in-human Trial
First-in-human (FIH) trial is the clinical study that evaluates the initial human exposure to an investigational drug or the combination of multiple (investigational) drugs. The primary objectives of the FIH trial are usually to investigate the dose- or exposure-dependent safety and tolerability of tested drugs in humans, and to understand the pharmacological interaction between the chemical entity and the human body, including the drug disposition and pharmacodynamic response.
Most of FIH trials are conducted in healthy young male volunteers, a relatively homogeneous population that is less prone to extra inter-subject variability. These trial participants have no underlying disease that could interfere with the conduct of the trial or the interpretation of drug- related toxicity, and withstand better the potential risk related to drug-induced toxicity.
The oncology FIH trials are, however, the exceptions. Experimental anti-cancer therapies are generally considered harboring higher hazard of severe toxicity, following the experiences of cytotoxic chemotherapy that brings significantly collateral damage to the healthy tissues. It raises ethical concerns when conducting oncology trials in healthy volunteers. Alternatively, these trial participants are usually cancer patients who have exhausted all current available therapeutic options. The ethical concern is justified by the potential life-saving benefit to the terminally ill patient that outweighs the experimental drug-related risk.
FIH trials of new investigational drugs are strictly regulated by health authorities, owing to the great uncertainty of pharmacological interaction between the molecule and the human body, and its corresponding risk to the trial participants.
One example is the 2006 incident of TGN1412. The investigational drug is a first-in-class IgG4 monoclonal antibody towards CD28, a T cell co-stimulatory receptor. This super-agonist, however, induced T-cell receptor (TCR) independent T-cell activation. During the FIH trial, a
31
starting dose of 0.1 mg/kg, which was 1/500 of dose level given to Cynomolgus monkeys [26], was administrated to six healthy volunteers intravenously. Within hours after drug infusion, all subjects experienced life-threatening systemic release of pro-inflammatory cytokines and subsequent multi-organ failure. The nonclinical studies did not predict the cytokine storm due to species-specific differences in the immune system activation in the analogous rodent model and the primate model [27]. It was found later in ex-vivo studies, that the predicted human blood concentration [28] at 0.1mg/kg was not only close to the saturate receptor occupancy [29], but also exceeded the maximum effect level in terms of cytokine release [30].
Another recent example is the 2016 FIH trial of BIA 10-2474 in France, resulting in one brain death and three with severer irreversible neurologic disorder during its multiple-ascending-dose phase [31]. The investigational drug candidate is a fatty acid amide hydrolase (FAAH) inhibitor that interacts with the endocannabinoid system, showing analgesic and anti-inflammatory activity in preclinical models. Following a relatively steep step of dose escalation, the unexplained high dose of 50mg daily, which is ten times above full target inhibitory dose, may fall into the non-linear pharmacokinetic range leading to more than expected drug accumulation [32, 33]. The corresponding off-target cross-reaction with several lipases that have the potential to cause metabolic dysregulation in the central nervous system is the most plausible explanation of the incident [34].
Such tragedies triggered a serial of guideline updates, including the 2007 European Medicine Agency (EMA) guideline for first-in-human trials [35], the 2010 International Council for Harmonisation of Technical Requirements for Pharmaceuticals (ICH) guideline for anticancer drug nonclinical assessment [36], and the very recent 2018 EMA FIH guideline [37]. An increasingly conservative attitude has been observed from the regulatory agencies. The latest EMA guideline emphasizes a risk-based approach with the implementation of safeguards and
32
risk mitigation plan, and application of scientific rationale to select the starting dose, subsequent dose escalation and pre-definition of maximum expected exposure.
II.2Starting Dose in Oncology First-In-Human Trial: Regulatory Perspectives
The goal of FIH starting dose selection is to establish a dosing regimen that is reasonably safe and tolerable in humans. Integrated nonclinical data package, including in-vivo and in-vitro pharmacokinetic (PK)/pharmacodynamic (PD) data, pharmacology characteristics from preclinical proof-of-principle and dosing schedule dependency studies, toxicology characteristics, serve as the basis of scientific justification for a safe starting dose in the FIM trial. Interspecies scaling of animal dose or exposure to human dose or exposure is mostly based on normalization to body surface area (BSA) or body weight [36].
In the ICH guideline, a starting dose is recommended for small molecules at 1/10 of the severely toxic dose in 10% of the rodents (STD10), or at 1/6 of the highest non-severely toxic dose (HNSTD) if non-rodent is the most appropriate species. For immune agonistic biologics, the minimally anticipated biologic effect level (MABEL) is recommended as the starting dose [36].
The latest EMA FIH guideline adopts a conservative approach for starting dose selection. It requires the determination of no observed adverse effect level (NOAEL) from nonclinical safety studies, MABEL and/or pharmacologically active dose (PAD) from nonclinical pharmacology studies. Using allometric scaling and physiologically based pharmacokinetic (PBPK) or PK/PD modeling and simulation approach, the human starting dose is to be decided on NOAEL, MABLE, or PAD, which should be reasonably safe and have a minimum PD effect. A safety factor (standard equal to 10) should be applied, taking into account potential risks related to the novelty of the drugs, characteristics of PD and safety findings, relevance/ translatability of animal models, the uncertainties in dose predictions, and the monitorability of clinical effects [37].
33
Table II.2.1 List of Approaches in Selecting Starting Dose in First-in-Human Trial
NOAEL No observed adverse effect level [38]: the highest dose level that does not produce a significant increase in adverse effects in comparison to the control group. Adverse effects that are biologically significant should be considered in the determination of the NOAEL [39]
MABEL Minimum anticipated biological effect level: exposure showing PD effects in the non-clinical pharmacology studies, including ex vivo and in vitro studies in human tissues
HNSTD Highest non-severely toxic dose: the highest dose level that does not produce evidence of lethality, life-threatening toxicities or irreversible findings
STD10 The severely toxic dose in 10% of the animals PAD Pharmacological active dose
II.3Efficiency of FIH Dose Scheme in Oncology Indications
Safety sets the upper boundary of the starting dose when designing the FIH trial, while efficacy sets the lower boundary. In oncology FIH studies, trial participants are usually terminal stage cancer patients who exhausted standard-of-care therapeutic options. Ethical ground and patient benefit consideration urge to find a starting dose that is reasonably safe and tolerable to use in humans, and sufficiently high that leads to meaningful pharmacodynamic response or clinical efficacy, which would minimize the exposure of trial participants to sub-therapeutic dose(s), and correspondingly shorten the dose-escalation steps.
Retrospectively, FDA conducted a series of examinations about the FIH starting doses of immune-activating agents [40], CD-3 bispecific antibodies [41], and antibody-drug conjugates [42], based on their database of IND (investigational new drug) applications that have completed phase I studies. FIH doses computed from preclinical toxicology studies (HNSTD, STD10, and NOAEL) and pharmacology studies (MABEL or PAD) were compared to the respective maximum tolerated dose (MTD), or the highest human dose (HHD) if MTD was not determined in the FIH trial.
34
Among 27 reviewed immune-activating agents including checkpoint inhibitors and immune stimulators, 44% of the antibodies had their starting doses 100-1000 folds lower than the maximum tolerated doses (MTD) or the highest human doses (HHD) established in the FIH trial. Preclinical toxicology-based rule was shown over-estimating the safety starting dose in some occasions: scaling from 1/6th the HNSTD or 1/10th the NOAEL predicted starting doses exceeding MTD or HHD for several antibodies, raising significant safety concerns. On the other hand, preclinical pharmacology-based rule had adequate performance: doses related to 20%~80%
in vitro receptor occupancy (RO) or antigen-binding (AB) had acceptable toxicity for all reviewed antibodies. It is to note that 20% RO /AB was the commonly used industrial standard of MABEL, and 80% RO corresponds to the safe pharmacology activity level for TGN1412, a super immune stimulator that reshaped the FIH trial guidelines. At RO / AB saturation level, the toxicity was yet acceptable for all except two antibodies with Fc modification and enhanced antibody-dependent cell-mediated cytotoxicity (ADCC) [40].
For CD3 bispecifics that bind to CD3 on T-cells and a second surface antigen on tumor cells resulting in T-cell activation and tumor cell lysis [43], drug-related toxicities were evident in pharmacologically-relevant (effective binding to both antigens) animal species for all 17 examined cases. Comparing to checkpoint inhibitors and immune stimulants, the animal tolerant doses of CD3 bispecifics were significantly lower [40], but yet generally better tolerated in non-human primates than in humans, likely due to the low expression level of antigens in animals. Doses computed from 10% RO, 1/6th the HNSTD, or 1/10th NOAEL scaled to humans by BSA or BW were found exceeding or too close to the MTD / HHD, thus consider not appropriate. Alternatively, doses corresponding to 10% - 30% PA was an acceptable approach to generate safe starting dose, but in some cases, triggered numerous dose-ascending steps to reach MTD [41].
35
Antibody-drug conjugates (ADC) is an emerging anti-cancer therapeutic modality that combines the selectivity of targeted therapy with the potency of cytotoxic agents [44], by linking monoclonal antibody targeting tumor-specific antigen with small molecule payloads.
Among the 20 examined cases, preclinical toxicity result-based FIH dose selection approaches, including 1/6th of the HNSTD in non-human primates or 1/10th of the STD10 in rodents scaled by BSA, were shown providing an adequate balance of acceptable safety/tolerability and dose escalation efficiency from starting dose to MTD. It was also found that the human MTDs are in close range for ADCs with the same payload, linkers, drug-antibody ratio (DAR) and dose frequency, but independent from antibody isotype and targeted antigen, indicating a limited contribution of antibody-mediated effector function (e.g., ADCC) to the overall toxicity. Prior clinical experience from other ADCs using the same payload, linker, and DAR, if available, is valuable data to inform the starting dose selection and escalation plan [42].
II.4Dose escalation and recommended phase II dose
FIH dose-escalation trial is designed to conclude a recommended phase II dose (RP2D) for further clinical development. From the starting dose, the dose-escalation scheme follows either a rule-based design or a model-based design. In a rule-based design, dose-levels are escalated according to a pre-specified algorithm until dose-limiting toxicity occurs, e.g., the classical 3+3 or modified 3+3 design and the accelerated titration design. At the end of dose escalation, the maximum tolerated dose (MTD) is usually recommended for phase II studies if the agent moves further into later development. In a model-based design, the dose escalation steps are informed by a statistical model that describes the dose-safety relationship with toxicity data from all enrolled patients using Bayesian approach. The model-based design requires less patients in each escalation step, and provides a more robust prediction of the RP2D with confidence interval.
36
Targeted agents exhibiting selectivity toward signal cascading pathway relevant to tumor cell proliferation, migration, and apoptosis, or immuno-modulation that recognize and suppress tumor cells, sometimes adopt an alternative strategy of selecting RP2D. With targeted agents, higher dose does not necessarily provide further treatment benefit after saturation of the target engagement, but may increase the probability of off-target toxicity. The RP2D strategy then shifts from MTD to the optimal biological dose (OBD), which is defined as the lowest dose yielding maximum efficacy and acceptable toxicity.
II.5Bayesian Approaches
The growing importance of Bayesian design in oncology FIH dose-escalation trial is rooted in the ethical guidepost of the trial itself: finding the right dose with the smallest possible sample to minimize the number of trial patients under sub-therapeutic doses. O’Quigley pioneered Bayesian design in phase I trial with continual reassessment method (CRM) [45-47]. It treats each patient sequentially with a dose corresponding to the target toxicity level, which is best supported by the continuously updated parametric dose - toxicity model. The MTD is then estimated based on the dose-toxicity model that is expected to perform well near the MTD.
Later variations include modified CRM that groups patients to update statistical inference and dose-finding decisions [48], escalation with overdose control [49], ordinal toxicity interval design [50], modified toxicity probability interval design [51]. The primary attribute of the Bayesian approach is that the uncertainty of the dose-toxicity relationship and the corresponding estimation of MTD is reduced by the accumulating knowledge from the trial, as one moves from prior to posterior.
With the Bayesian framework, the dose-finding could also be informed by joint modeling of toxicity and biomarker [52]. Such an integrated Bayesian model accounting for both efficacy and toxicity effectively recommended OBD in a seamless phase I/II trial for targeted agents
37
[53]. Bayesian approach could also accommodate more sophisticated trial designs, including basket trial[54], umbrella trial [55], and platform trial, due to its ability to integrate prior information across treatments and patient population.
II.6Pharmacokinetic / Pharmacodynamic Modeling to Inform FIH Dose Decision
Mathematical modeling and simulation is strongly endorsed by regulatory authorities to aid the FIH dose decision. The modeling framework provides a quantitative platform to integrate multiple dimensional data, including in vitro receptor occupancy, target expression/binding, in vivo pharmacokinetic, pharmacodynamic, efficacy, and toxicology data.
Pharmacokinetic modeling is a powerful tool to predict human exposure. Human clearance of small molecule could be reasonably predicted with partition into renal and/or biliary clearance from allometric scaling [56], and hepatic clearance from human in vitro metabolic data [57].
For large molecules within linear PK range, allometric scaling based on single specie primate data also provide adequate predictive power, when fixing the scaling exponent of clearance to 0.8~0.9 [58-60]. Target-mediate drug disposition (TMDD) model [61] addresses the nonlinear kinetics of monoclonal antibody with a mechanistic description of receptor-mediated endocytosis, a rate-limiting step for most antibodies. An alternative tool to project nonlinear PK is the mixed linear and Michaelis-Menten elimination model [62], in which the human PK parameters are either scaled allometrically or assumed to be similar to monkey parameters.
Physiology-based pharmacokinetic (PBPK) modeling offers a mechanistic tool both for small molecule [63, 64] and large molecule [65-68], which transcribes anatomical, physiological and physiochemical description of the complex drug absorption, disposition, metabolism and elimination (ADME) process.
Regardless of the model complexity, the predictive value relies on the relevance of the data derived from preclinical investigation.
38 II.7A schematic Approach of FIH Dose Selection
The development of novel cancer therapeutics at first human exposure should always adopt a risk-based approach, with appropriate justification based on the mode of action, biological activity, reversibility and duration of the activity, the novelty of target, preclinical toxicology findings, clinical monitorability and uncertainty in animal-to-human translation. As a general rule, the more uncertainty or less translatability from preclinical to clinical, the more caution to apply to the starting dose.
In conclusion, a pharmacologically active but reasonably safe dose regimen is targeted for oncology FIH trials. With a good understanding of the underlying mechanism of action, the totality of preclinical toxicology and pharmacology data, together with literature information and clinical experience of other drug candidates targeting the same antigen/signaling pathway ensures the appropriateness of the FIH dose selection.
II.8References
25. Klaassen, C.D., L.J. Casarett, and J. Doull, Casarett and Doull's toxicology : the basic science of poisons. 8th ed. ed. 2013, New York: McGraw-Hill Education / Medical.
26. Suntharalingam, G., et al., Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N Engl J Med, 2006. 355(10): p. 1018-28.
27. Hunig, T., The storm has cleared: lessons from the CD28 superagonist TGN1412 trial. Nat Rev Immunol, 2012. 12(5): p. 317-8.
28. Expert Scientific, G., Expert Scientific Group on Phase One Clinical Trials-Final Report.
http://www.tsoshop.co.uk, 2006.
29. Waibler, Z., et al., Toward experimental assessment of receptor occupancy: TGN1412 revisited.
J Allergy Clin Immunol, 2008. 122(5): p. 890-2.
30. Romer, P.S., et al., Preculture of PBMCs at high cell density increases sensitivity of T-cell responses, revealing cytokine release by CD28 superagonist TGN1412. Blood, 2011. 118(26): p.
6772-82.
31. Kerbrat, A., et al., Acute Neurologic Disorder from an Inhibitor of Fatty Acid Amide Hydrolase.
N Engl J Med, 2016. 375(18): p. 1717-1725.
32. Moore, N., Lessons from the fatal French study BIA-10-2474. BMJ, 2016. 353: p. i2727.
33. Chaikin, P., The Bial 10-2474 Phase 1 Study-A Drug Development Perspective and Recommendations for Future First-in-Human Trials. J Clin Pharmacol, 2017. 57(6): p. 690-703.
34. van Esbroeck, A.C.M., et al., Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474. Science, 2017. 356(6342): p. 1084-1087.
35. Agency, E.M., GUIDELINE ON REQUIREMENTS FOR FIRST-IN-MAN CLINICAL TRIALS FOR POTENTIAL HIGH-RISK MEDICINAL PRODUCTS. 2007.
