Clinical trials study design, endpoints and biomarkers, drug safety

CHAPTER The Origins of Drugs I.  INTRODUCTION Drugs have a number of origins, as outlined by the bullet points: Natural products, for example, chemicals from plants and microorganisms Analogues of naturally occurring chemicals, where these chemicals reside in the biosynthetic pathways of mammals Antibodies that bind to naturally occurring targets in the body Discovery that an existing drug, established as effective for a first disease, is also effective for treating an unrelated second disease Drugs identified by screening libraries of chemicals Some drugs are based on natural products, where the natural products were known to have pharmacological effects The term “natural products” is a term of the art that generally refers to chemicals derived from plants, fungi, or microorganisms Drugs that are derived from natural products, or that actually are natural products, include warfarin (1) penicillin (2,3) cyclosporin (4) aspirin (5,6) paclitaxel (7) fingolimod (8) and reserpine (9) Many other drugs have structures based on chemicals that occur naturally in the human body, that is, where the drugs are analogues of these chemicals These include analogues of intermediates or final products of biosynthetic pathways Drugs that are analogues of chemicals in biosynthetic pathways include methotrexate, cladribine, and ribavirin Still other drugs originated by first identifying a target cell, or target protein, and then by preparing antibodies that bind to that target Once a target protein is identified, this target protein (or a derivative of it) can be used as a vaccine Moreover, l l l l l  Wardrop D, Keeling D T   he story of the discovery of heparin and warfarin Br J Haematol 2008;141:757–763  Diggins FW  The true history of the discovery of penicillin, with refutation of the misinformation in the literature Br J Biomed Sci 1999;56:83–93  Fleming A On the antibacterial action of cultures of a penicillium, with special reference to their use in the isolation of B influenzae 1929 Bull World Health Organ 2001;79:780–790   Heusler K, Pletscher A The controversial early history of cyclosporin Swiss Med Wkly 2001;131:299–302   Lafont O From the willow to aspirin Rev Hist Pharm (Paris) 2007;55:209–216  Mahdi JG, Mahdi AJ, Mahdi AJ, Bowen ID The historical analysis of aspirin discovery, its relation to the willow tree and antiproliferative and anticancer potential Cell Prolif 2006;39:147–155  Socinski MA Single-agent paclitaxel in the treatment of advanced non-small cell lung cancer Oncologist 1999;4:408–416  Adachi K, Chiba K FTY720 story Its discovery and the following accelerated development of sphingosine 1-phosphate receptor agonists as immunomodulators based on reverse pharmacology Perspect Medicin Chem 2007;1:11–23   Rao EV Drug discovery from plants Curr Science 2007;93:1060 Clinical Trials DOI: 10.1016/B978-0-12-391911-3.00001-3 © 2012 Elsevier Inc All rights reserved Clinical Trials drugs are also derived by using a screening assay and by testing hundreds or thousands of purified candidate compounds using that assay Where the screening method is automated, the method is called high-throughput screening The screening assay may consist of tumor cells that are cultured in vitro, where a robot determines if the candidate drug inhibits a particular enzyme in the tumor cell, or if the candidate drug kills the tumor cell II.  STRUCTURES OF DRUGS Knowledge of drug structure is important to the investigator and to clinical trial personnel for a number of reasons First, the issue of whether a drug is hydrophobic or hydrophilic will dictate the excipient that needs to be used The structure can also provide an idea of stability during long-term storage and, for example, if the drug is sensitive to light Second, the structure may dictate the route of administration, and enable a prediction of pharmacokinetics of the drug and pathways of metabolism, transport, and excretion Third, the structure of the drug, and more particularly the class of compound, can help the investigator predict adverse events that might be expected from the drug Fourth, FDA-submissions, such as the Investigational New Drug and Investigator’s Brochure, typically contain a picture of the drug structure a.  Origins of warfarin Warfarin is a drug that is widely used to prevent blood clotting, for example in people at risk of heart attacks or strokes (10) A natural product produced during the spoiling of sweet clover inspired warfarin’s design The drug was not named after any kind of warfare, even though it is used in warfare against mice and rats It was named after the Wisconsin Alumni Research Foundation Spoiled sweet clover contains coumarin, a compound that inhibits an enzyme in the liver, where the end-result is impaired blood clotting Blood clotting factors are biosynthesized in the liver, and then released into the bloodstream Farmers in the mid-west found that cattle bled to death during the process of de-horning, where the cattle had eaten spoiled sweet clover Eventually, one particular farmer in Wisconsin brought a bucket of unclotted blood to researchers at the University of Wisconsin The researchers examined blood, as well as samples of spoiled sweet clover, and discovered that the culprit was dicoumarol, a degradative product of coumarin Researchers synthesized and tested about 50 analogues of this compound The analogues were tested in 10  Gage BF, van Walraven C, Pearce L, et al Selecting patients with atrial fibrillation for anticoagulation: stroke risk stratification in patients taking aspirin Circulation 2004;110:2287–2292 The Origins of Drugs rabbits It was discovered that the best analogue was warfarin (11) Warfarin is also the active ingredient in rodent poison O O OH O Warfarin b.  Origins of methotrexate and 5-fluorouracil The natural substrate of one particular enzyme, dihydrofolate reductase, inspired the design of methotrexate This natural substrate is dihydrofolic acid (12) Dihydrofolic acid is the end-product of the biosynthetic pathway of folates (13) Anti-cancer drugs that inhibit dihydrofolate reductase were designed by synthesizing and screening chemicals that resembled dihydrofolate (14,15,16) Methotrexate, which is an analogue of dihydrofolic acid and also an analogue of folic acid, inhibits dihydrofolic acid reductase Another anti-folate drug used in oncology is 5-fluorouracil Fluorouracil was invented by Charles Heidelberger (17,18) The drug was developed on the basis of findings in the 1950s that cancer cells incorporated a larger amount of the uracil base into the DNA than normal cells In testing a number of halogen substituted uracils, 5-fluorouracil appeared to be the most active and promising drug Fluorouracil is a suicide inhibitor of thymidylate synthase This means that the enzyme’s own catalytic activity results in the activation of the drug, where this activation causes the drug to react covalently with the enzyme, thereby destroying the enzyme’s catalytic activity 11   Link KP The discovery of dicumarol and its sequels Circulation 1959;19:97–107  Folic acid is used as a vitamin supplement and for enzymatic studies of dihydrofolic acid reductase But folic acid is not a naturally occurring chemical Folic acid is formed during the breakdown of dihydrofolic acid, upon exposure to oxygen Dihydrofolic acid is a natural product made by microorganisms and plants 13  Brown GM, Williamson JM Biosynthesis of riboflavin, folic acid, thiamine, and pantothenic acid Adv Enzymol Relat Areas Mol Biol 1982;53:345–381 14  Friedkin M Enzymatic aspects of folic acid Annu Rev Biochem 1963;32:185–214 15   Bertino JR The mechanism of action of folate antagonists in man Cancer Res 1963;23:1286–1306 16   Brody T Folic acid, In: Machlin LJ, ed Handbook of Vitamins Marcel Dekker, Inc New York, 1990; pp 453–489 17  Muggia FM, Peters GJ, Landolph JR Jr XIII International Charles Heidelberger Symposium and 50 Years of Fluoropyrimidines in Cancer Therapy held on September to 8, 2007 at New York University Cancer Institute, Smilow Conference Center Mol Cancer Ther 2009;8:992–999 18  Heidelberger C On the rational development of a new drug: the example of the fluorinated pyrimidines Cancer Treat Rep 1981;65 (Suppl 3):3–9 12 Clinical Trials H2N N N N N N O H N NH2 OH O O Methotrexate OH c.  Origins of ribavirin Ribavirin was discovered by synthesizing analogues of compounds participating in the pathways of nucleotide biosynthesis In designing, synthesizing, and testing a variety of analogues of intermediates in nucleotide biosynthetic pathways, the result was the discovery of ribavirin, also known as virazole (19,20) Ribavirin is the standard of care used for treating hepatitis C virus (HCV) infections O NH2 N N N HO O OH OH Ribavirin d.  Origins of paclitaxel Paclitaxel (Taxol®), an anti-cancer drug, was discovered in extracts of the Pacific yew tree, Taxus brevifolia In 1963, a crude extract from Pacific yew bark was found to have activity against tumors in experimental animals (21) In 1991, the active component, paclitaxel, was approved by the FDA as an anti-cancer drug Paclitaxel, which is in a class of drugs 19  Witkowski JT, Robins RK, Sidwell RW, Simon LN Design, synthesis, and broad spectrum antiviral activity of 1-beta-D-ribofuranosyl-1,2,4-triazole-3-carboxamide and related nucleosides J Med Chem 1972;15:1150–1154 20  Te HS, Randall G, Jensen DM Mechanism of action of ribavirin in the treatment of chronic hepatitis C Gastroenterol Hepatol 2007;3:218–225 21  Socinski MA Single-agent paclitaxel in the treatment of advanced non-small cell lung cancer Oncologist 1999;4:408–416 The Origins of Drugs called taxanes, acts on the cytoskeleton of the cell Specifically, the drug acts on tubulin, disrupts the normal behavior of the cytoskeleton in mediating cell division, and causes cell death (22) Docetaxel (Taxotere®) is a semi-synthetic analogue of paclitaxel (23) having a mechanism and anti-cancer properties similar to those of paclitaxel Docetaxel can be synthesized using a precursor extracted from needles of the European yew, Taxus baccata (24) O O N O OH CH2 O H2C H H 2C CH2 O O H2C O OH O H O CH2 O OH O Paclitaxel e.  Origins of cladribine Cladribine (2-chloro-2-deoxyadenosine) is a small molecule that is a nucleotide analogue Cladribine is an analogue of deoxyadenosine After administration, cladribine enters various cells and once inside the cell, an enzyme catalyzes the attachment of three phosphate groups The result is the conversion of cladribine to cladribine triphosphate Cladribine triphosphate, in turn, inhibits DNA synthesis, inhibits DNA repair, and results in apoptosis (death of the cell) The drug is most active in cells with high levels of the deoxycytidine kinase, such as lymphocytes (25) Cladribine is used for treating multiple sclerosis and a type of leukemia (hairy cell leukemia) The connection between deoxynucleotides and killing lymphocytes, as it applies to cladribine, is as follows Inherited deficiencies of the enzyme adenosine deaminase interfere with lymphocyte development while sparing most other organ systems (26) The 22  Pusztai L Markers predicting clinical benefit in breast cancer from microtubule-targeting agents Ann Oncol 2007;18 (Suppl 12):xii,15–20 23  Bissery MC, Guénard D, Guéritte-Voegelein F, Lavelle F Experimental antitumor activity of taxotere (RP 56976, NSC 628503), a taxol analogue Cancer Res 1991;51:4845–4852 24  Verweij J Docetaxel (Taxotere): a new anti-cancer drug with promising potential? Br J Cancer 1994;70:183–184 25  Piro LD, Carrera CJ, Beutler E, Carson DA 2-Chlorodeoxyadenosine: an effective new agent for the treatment of chronic lymphocytic leukemia Blood 1988;72:1069–1073 26  Carson DA, Kaye J, Seegmiller JE Lymphospecific toxicity in adenosine deaminase deficiency and purine nucleoside phosphorylase deficiency: possible role of nucleoside kinase(s) Proc Natl Acad Sci USA 1977;74:5677–5681 Clinical Trials accumulation of deoxyadenosine nucleotides in the lymphocytes, that is, in lymphocytes of people suffering from adenosine deaminase deficiency, reduces the number of lymphocytes As a consequence, the patients suffer from severe immunodeficiency Carson et al (27) realized that the elimination of adenosine deamidase activity can halt lymphocytes that are pathological, such as the lymphocytes in leukemia (leukemia is a cancer of lymphocytes) This elimination was accomplished by cladribine Cladribine, in effect, mimicks the inherited disease (adenosine deaminase deficiency) because cladribine resists the effects of adenosine deaminase Cladribine naturally resists deamination catalyzed by adenosine deaminase (For cladribine to be effective in destroying lymphocytes, it is not necessary that patients be suffering from adenosine deaminase deficiency.) Just as the normally occurring deoxyadenosine kills lymphocytes in people with the genetic disease of adenosine deaminase deficiency, cladribine kills lymphocytes when administered to normal humans (28) It was about ten years after the use of cladribine to treat leukemia that cladribine was first used to treat multiple sclerosis (29,30) To summarize, the pathway of discovery of cladribine for multiple sclerosis was as follows First, it was known that an inherited genetic disease involved the accumulation of deoxyadenosine nucleotides in the cell, and resulted in death of lymphocytes Second, researchers developed a drug that, when administered to a human subject, mimicked the effects of this disease (due to the inability of adenosine deaminase to act on the drug) Third, the drug was used to treat leukemia Fourth, the drug was used to treat multiple sclerosis (31) NH2 N N HO O OH 27 N N CI H  Carson DA, Wasson DB, Taetle R,Yu A Specific toxicity of 2-chlorodeoxyadenosine toward resting and proliferating human lymphocytes Blood 1983;62:737–743 28  Piro LD, Carrera CJ, Beutler E, Carson DA 2-Chlorodeoxyadenosine: an effective new agent for the treatment of chronic lymphocytic leukemia Blood 1988;72:1069–1073 29  Sipe JC, Romine JS, Koziol JA, McMillan R, Zyroff J, Beutler E Cladribine in treatment of chronic progressive multiple sclerosis Lancet 1994;344:9–13 30  Beutler E, Koziol JA, McMillan R, Sipe JC, Romine JS, Carrera CJ Marrow suppression produced by repeated doses of cladribine Acta Haematol 1994;91:10–15 31  Giovannoni G, Comi G, Cook S, et al A placebo-controlled trial of oral cladribine for relapsing multiple sclerosis New Engl J Med 2010;362:416–426 The Origins of Drugs f.  Origins of drugs in high-throughput screening A number of drugs and drug candidates were discovered by high-throughput screening Wigle et al (32) describe antibiotics that were found by high-throughput screening White et al (33) describe drugs for treating inflammatory diseases that were discovered by highthroughput screening.Von Hoff et al (34) and others (35) describe a drug used for treating cancer that was identified by high-throughput screening g.  Origins of therapeutic antibodies Antibodies designed with the aid of animal models are used for treating various cancers and immune diseases For example, antibody drugs include trastuzumab (Herceptin®) (36) which binds to epidermal growth factor, and which is used to treat breast cancer Antibody drugs also include bevacizumab (Avastin®) (37) which binds to vascular endothelial growth factor receptor (VEGF), and is used to treat a variety of cancers Moreover, an antibody drug used to treat various immune diseases is natalizumab (Tysabri®) (38) This antibody binds to a protein called integrin, which occurs on the surface of white blood cells Natalizumab is used to treat two immune diseases, namely, multiple sclerosis and Crohn’s disease Developing antibody drugs includes the step of refining the polypeptide sequence of the antibody into a drug suitable for administering to humans (39,40,41) This refinement step is called humanization (42) Humanization refers to the process of using genetic engineering to convert any protein of animal origin, to a protein that can be injected into people, where the injected protein fails to elicit an immune reaction against itself 32  Wigle TJ, Sexton JZ, Gromova AV, et al Inhibitors of RecA activity discovered by high-throughput screening: cellpermeable small molecules attenuate the SOS response in Escherichia coli J Biomol Screen 2009;14:1092–1101 33  White JR, Lee JM,Young PR, et al Identification of a potent, selective non-peptide CXCR2 antagonist that inhibits interleukin-8-induced neutrophil migration J Biol Chem 1998;273:10095–10098 34  Von Hoff DD, LoRusso PM, Rudin CM, et al Inhibition of the hedgehog pathway in advanced basal-cell carcinoma New Engl J Med 2009;361:1164–1172 35  Zhou BB, Zhang H, Damelin M, Geles KG, Grindley JC, Dirks PB Tumour-initiating cells: challenges and opportunities for anticancer drug discovery Nat Rev Drug Discov 2009;8:806–823 36  Verma S, Lavasani S, Mackey J, et al Optimizing the management of her2-positive early breast cancer: the clinical reality Curr Oncol 2010;17:20–33 37  Eskens FA, Sleijfer S T   he use of bevacizumab in colorectal, lung, breast, renal and ovarian cancer: where does it fit? Eur J Cancer 2008;44:2350–2356 38  Coyle PK The role of natalizumab in the treatment of multiple sclerosis Am J Manag Care 2010; 16(Suppl 6):S164–S170 39  Kent SJ, Karlik SJ, Cannon C, et al A monoclonal antibody to alpha integrin suppresses and reverses active experimental allergic encephalomyelitis J Neuroimmunol 1995;58:1–10 40  Yednock TA, Cannon C, Fritz LC, et al Prevention of experimental autoimmune encephalomyelitis by antibodies against alpha beta integrin Nature 1992;356:63–66 41  Brody T Multistep denaturation and hierarchy of disulfide bond cleavage of a monoclonal antibody Analyt Biochem 1997;247:247–256 42   Presta LG Molecular engineering and design of therapeutic antibodies Curr Opin Immunol 2008;20:460–470 Clinical Trials Antibodies take the form of four polypeptides, two light chains and two heavy chains, as indicated in the diagram below The first light chain and first heavy chain are covalently attached to each other by disulfide bonds, to form a first complex The second light chain and second heavy chain are covalently attached to each by disulfide bonds to form a second complex The first complex and second complex are also covalently attached to each other by way of disulfide bonds Light chain Heavy chain Heavy chain Light chain As an example of an antibody drug, the amino acid sequence of the light chain and the amino acid sequence of the heavy chain of trastuzumab are shown below (43) The amino acid sequence of the light chain of trastuzumab, as found at the cited accession numbers (44,45) is shown below The light chain, shown below, has 214 amino acids DIQMTQSPSSLSASVGDRVTITCRASQDVNTAVAWYQQKPGKAPKLLIYSASFLYSGVPSRFSGSRSGTDFT LTISSLQPEDFATYYCQQHYTTPPTFGQGTKVEIKRTVAAPSFIFPPSDEQLKSGTASVVCLLNNFYPREAK VQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC The amino acid sequence of the heavy chain of this antibody, which has 451 amino acids and can be found at the cited accession number (46) is shown below EVQLVESGGGLVQPGGSLRLSCAASGFNIKDTYIHWVRQAPGKGLEWVARIYPTNGYTRYADSVKGRFTISA DTSKNTAYLQMNSLRAEDTAVYYCSRWGGDGFYAMDYWGQGTLVTVSSASTKGPSVFPLAPSSKSTSGGTAA LGCLVKDYFPEPVTVSWNSGALTSGVHTFPAVLQSSGLYSLSSVVTVPSSSLGTQTYICNVNHKPSNTKVDK KVEPPKSCDKTHTCPPCPAPELLGGPSVFLFPPKPKDTLMISRTPEVTCVVVDVSHEDPEVKFNWYVDGVEV HNAKTKPREEQYNSTYRVVSVLTVLHQDWLNGKEYKCKVSNKALPAPIEKTISKAKGQPREPQVYTLPPSRD ELTKNQVSLTCLVKGFYPSDIAVEWESNGQPENNYKTTPPVLDSDGSFFLYSKLTVDKSRWQQGNVFSCSVM HEALHNHYTQKSLSLSPGK 43  Fong S, Hu Z T   herapeutic anti-HER2 antibody fusion polypeptides U.S Pat Appl Publ 2009/0226466 2009;Sept 10   Cho HS, Mason K, Ramyar KX, et al GenBank Accession No PDB:1N8Z_A (submitted November 21, 2002) 45   http://www.drugbank.ca/drugs/DB00072 46  Trastuzumab (DB00072) DrugBank Accession No DB0072 Creation date June 13, 2005, updated June 2, 2009 44 The Origins of Drugs The three-dimensional structure of this antibody drug can be found at www.drugbank ca/drugs/DB00072 Let us dwell on the structure of the light chain and heavy chain for a moment In testing and marketing any polypeptide drug, pharmaceutical companies are concerned with the following drug stability issues First, it is the case that long-term storage of polypeptides results in the spontaneous deamination of residues of glutamine (Q) and asparagine (N) Deamination can occur at various steps in the manufacturing process, during shipment, and during storage Also, oxidation of cysteine (C) residues can occur during manufacturing, shipping, and storage These types of damage may lower the potency of polypeptide drugs The reader will be able to find the locations of Q, N, and C in the polypeptide chains of trastuzumab III.  THE 20 CLASSICAL AMINO ACIDS The following reviews the 20 classical amino acids Twenty classical amino acids exist, and these are listed, along with their abbreviations, in Table 1.1 The non-classical amino acids include homocysteine, selenocysteine (47) methionine sulfoxide, ornithine, gamma-carboxyglutamate (GLA) (48) phosphotyrosine, hydroxyproline (49) sarcosine, and betaine A protein is a long polypeptide that is a linear polymer of amino acids, typically about 100 to 500 amino acids in length The term oligopeptide refers to shorter polymers of amino acids in the range of about ten to 50 amino acids Some nonclassical amino acids, such as homocysteine, exist only in the free state, and not become incorporated into any polypeptide But other non-classical amino acids, such as gamma-carboxyglutamate and phosphotyrosine, occur in naturally occurring proteins because of a process called post-translational modification Knowledge of the amino acids is needed to understand the following pharmacological issues: Stability during manufacturing and storage Point of attachment of polyethylene glycol (PEG) Unwanted immunogenicity Immunogenicity that is desired and essential for drug efficacy The following concerns in vitro stability For drugs that are oligopeptides or proteins, stability during manufacturing and storage is an issue because of spontaneous deamidation Aswad and co-workers have detailed the deamidation of biologicals l l l l 47   Brody T Nutritional Biochemistry, 2nd ed San Diego, CA: Academic Press 1999;21, 825–827  Brody T, Suttie JW Evidence for the glycoprotein nature of vitamin K-dependent carboxylase from rat liver Biochim Biophys Acta 1987;923:1–7 49   Brody,T Nutritional Biochemistry, 2nd ed San Diego, CA: Academic Press 1999;21, 619–623 48 Abbreviations and Definitions tid q3w USC; U.S.C USPTO WHO “ter in die,” which is Latin for three times a day every three weeks United States Code United States Patent and Trademark Office World Health Organization xxxi INDEX A ABC transporters, 76–78 Abrial study, 148 AC, see Active control Active control (AC), 24, 27, 44–46 Active placebo, 133–134 Acute lymphocytic leukemia (ALL) clinical features, 284–287 cytogenetics for diagnosis and prediction, 300–304, 323 minimal residual disease, 317–320 Acute myeloid leukemia (AML) clinical features, 289–291 cytogenetics for diagnosis and prediction, 300–302 Acute promyelocytic leukemia (APL), 291–292, 323 ADCC, see Antibody-dependent cell cytotoxicity Add-on design active control, 45 Adjuvant therapy advantages over neoadjuvant therapy chronic drug administration, 276 metastasis risk reduction, 275 staging accuracy, 276 meanings of adjuvant, 276–277 neoadjuvant therapy advantages, see Neoadjuvant therapy overview, 271–272 Adverse event (AE) adverse drug reaction, 425 anticipation in clinical study design, 419–420 cause-and-effect data from raw data, 435–436 classification anticipated versus unanticipated, 431–435 disease induction versus study drug induction, 426–427 statistician considerations, 427 Data and Safety Monitoring Committee, see Data and Safety Monitoring Committee Dear Healthcare Professional letters in risk minimization acne medications, 456–457 appetite suppressants, 457 birth control pills, 455–456 example, 454–455 overview, 452–454 definitions, 423–426 dose modification, 420–423 examples, 418–419 intent to treat analysis, 427–431 mechanism of action and expected adverse drug reactions, 473–474 monitoring and evaluation Case Report Form follow up reports, 446–447 writing style, 444–445 CTCAE dictionary, 442 data capturing, transmission, and evaluation, 445–449 data manager, 441–442 grading, 442–443 missing data examples, 443–444 overview, 440–441 post-marketing report CIOMS I form, 450–451 MedWatch form, 448–450 surveillance, 451–452 overview, 415–417 paradoxical adverse drug reactions bronchial constriction drugs, 440 cancer chemotherapy, 437–438 growth factor therapy, 438–439 depression drugs, 439 overview, 436 patient-reported outcomes, see Patient-reported outcomes run-in period and baseline adverse event detection, 52 serious adverse event, 424 unexpected adverse drug reaction, 425 Advisory Committee, meetings, 600 AE, see Adverse event ALL, see Acute lymphocytic leukemia Allin study, 347 Allocation concealment blinding comparison, 112–113 625 626 Index Allocation (Continued ) importance, 113–114 enrollment over time, 122–123 manual techniques coin toss, 118 sealed envelopes, 115–119 overview, 112–113 Alzheimer’s disease, randomization code breaking example, 119–120 Amendments, Clinical Study Protocol, 48–50 Amino acids, characteristics and pharmacological importance, 9–11 AML, see Acute myeloid leukemia Anatomical Therapeutic Chemical (ATC) system, 565 Antibody drugs, origins, 7–9 Antibody-dependent cell cytotoxicity (ADCC), 481, 486, 498–499 APL, see Acute promyelocytic leukemia Approval Letter, 603 Arthritis, health-related quality of life, 404 Asthma, immune response, 506 ATC system, see Anatomical Therapeutic Chemical system Atherosclerosis, C-reactive protein as biomarker, 349–351 5-Aza-deoxycytidine, myelodysplastic syndrome management, 299 B Bacillus Calmette-Guerin (BCG), 490 Baker v St Agnes Hospital, 569 Baselga schema, 31 Basso study, 318–320 BCG, see Bacillus Calmette-Guerin BCR-ABL, see Philadelphia chromosome Bedikian study, 234–235 Belmont Report, 540 Bepler study, 259–261 Berek study, 159–160 Berthold study, 148–149 Bevacizumab, 41 Bezjak study, 396 Biomarkers breast cancer, 332–337 C-reactive protein, see C-reactive protein circulating tumor cells, 338 colorectal cancer, 337–340 DNA microarray colon cancer, 343–344 hepatocellular carcinoma, 344–345 ovarian cancer, 342–343 overview, 341 hepatitis C virus infection, 381–382 mass spectrometry, 349 measurement relative to chemotherapy, 322 overview, 327–328 predictive versus prognostic, 328–330 prognostic biomarker validation, 259–261 study design inclusion, 330 surrogate marker criteria, 331 thymidine phosphorylase as survival biomarker, 248–249 Biostatistics comparison analysis, 175–176 hazard ratio calculation, 171–172 overview, 169–170 Kaplan-Meier plot censoring data, 168–169, 172 Holm study, 166–168 overview, 165–166 time of enrollment and data, 172–174 one-tailed test, 176–177 P-value calculation, 166, 168, 180–186 hypothesis testing, 177–180 population, 174–175 sample, 174–175 superiority analysis versus non-inferiority analysis, 187–190 two-tailed test, 176–177 Black box warning, 565–567 Blinding allocation concealment versus blinding, 112–113 bias sources, 124 overview, 123–125 Reck schema, 38–40 Blood cancers, see Hematological cancers Blumenschein schema, 28–30 Boceprevir, 519 Bonomi study, 396 BRCA1, 332 Breast cancer biomarkers, 332–337 gene array as prognostic factor, 267–268 health-related quality of life, 397–398 Ring study, 258–259 staging Index ductal carcinoma in situ, 100–101 invasive breast cancer, 101 lobular carcinoma in situ, 100–101 TNM staging, 101–105 time to distant metastasis and prognostic factors in breast cancer gene array, 267–268 microRNA expression, 268 Bristol-Myers Co v Gonzales, 570 Bristol-Myers Squibb Co v Rhone-Poulenc Rorer, Inc., 620 C Capecitabine, thymidine phosphorylase as survival biomarker, 249 Cappuzzo study, 245–246 Caraceni study, 161 Carboplatin Maemondo study, 221–224 toxicity, 74, 76 Case Report Form (CRF) follow up reports, 446–447 writing style, 444–445 Cetuximab, 443–444 Chauffert study, 149 Chronic lymphocytic leukemia (CLL) clinical features, 287–288 cytogenetics for diagnosis and prediction, 307–308, 314–315 Chronic myeloid leukemia (CML) clinical features, 293–294 cytogenetics for diagnosis and prediction, 304–305 Chronic obstructive pulmonary disease (COPD) health-related quality of life, 405 immune response, 506 Cilloni study, 320–322 CIOMS, see Council of International Organizations of Medical Sciences Cisplatin, black box warning, 565–566 Cladribine multiple sclerosis management, 509–510 origins, 5–6 Clinical endpoint, 191 Clinical research associate (CRA), functions, 111, 441 Clinical Review, 601–602 Clinical Study Protocol (CSP) amendments, 48–50 inclusion/exclusion criteria, 64–65 overview, 63–64 randomization code breaking examples, 119–122 stratification of subjects, see Stratification CLL, see Chronic lymphocytic leukemia CML, see Chronic myeloid leukemia Cohen study, 337 Colorectal cancer biomarkers, 337–340 DNA microarray, 343–344 health-related quality of life, 390–393 staging, 97–100 supportive/palliative care in placebo arm, 136 Van Cutsem study, 230–234 Common Technical Document (CTD), 591–594 Consent forms administrative law, 535, 538–539, 541–543 analysis by medical community, 552 case law, 541 Coyne study informed consent form, 550–551 decision aids, 556–558 ethics, 540 Form E1594, 544–549 Guidance for Industry E6, 539–540, 551 language level, 543–544, 553–555 package insert comparison, 573 Phase I trials, 555–556 stopping treatment versus withdrawing from study, 558 Yellow Fever Commission, 536–537 Contract research organization (CRO), 445, 447 COPD, see Chronic obstructive pulmonary disease Council of International Organizations of Medical Sciences (CIOMS) adverse event classification, 426 CIOMS I form, 450–451 Coyne study, 549–552 CRA, see Clinical research associate C-reactive protein (CRP) biology, 345–346 biomarker applications atherosclerosis, 349–351 cancer, 347–349 overview, 345, 352–353 inflammatory bowel disease levels, 341 CRF, see Case Report Form CRO, see Contract research organization 627 628 Index Crohn’s disease C-reactive protein levels, 341 health-related quality of life, 404–405 immune response, 506 Cross-resistance, see Drug resistance CRP, see C-reactive protein CSP, see Clinical Study Protocol CTCAE dictionary, adverse event severity grading, 442–443 CTD, see Common Technical Document Cumulative toxicity, basis of exclusion, 74–76 Cytogenetic analysis, see Hematological cancers Cytokines, see also specific cytokines classification, 476–477 mechanism of action considerations in diseases with immune component, 476–478 modulation of immune system, 483–484 Czito schema, 30 D Data and Safety Monitoring Committee (DMC) Charter Board Meetings and Reports, 464–465 Board Membership, 463 Communications, 467 Conflict of Interest, 464 DMC Recommendations, 466–467 Introduction, 463 Participants, 465 Periodic Reports, 466 Review Materials, 465 Role of the Board, 463 Sponsor’s Decisions and Announcements, 468 Stopping Rules, 467 Term, 463 Timetable, 468 Unscheduled Meetings, 466 overview, 441, 460–462 Data manager, functions, 441–442 DC, see Dendritic cell Dear Healthcare Professional letter acne medications, 456–457 appetite suppressants, 457 birth control pills, 455–456 example, 454–455 overview, 452–454 Decision aid, informed consent, 556–558 Decision tree Baselga schema, 31 Katsumata schema, 31–35 run-in period, 59–60 Demetri study, 203–206 Dendritic cell (DC) classification, 487–488 functional overview, 478–481 hepatitis C virus response, 523–525, 532 multiple sclerosis role, 512–513 Depression, paradoxical adverse drug reactions of antidepressants, 439 DFS, see Disease-free survival Di Bisceglie study, 380–381 Dicumarol, package insert and liability protection, 569–570 Dilantin, package insert and liability protection, 570 Disease-free survival (DFS) add-on breast cancer studies Hayes study, 255–256 Romond study, 254–255 ambiguity, 253–254 neoadjuvant versus adjuvant therapy for rectal cancer, 257–258 overall survival comparison, 254–255 overview, 251–252 prognostic biomarker validation, 259–261 progression-free survival comparison, 252 Ring study, 258–259 DLT, see Dose-limiting toxicity DMC, see Data and Safety Monitoring Committee DNA microarray colon cancer, 343–344 cytogenetics confluence in hematological cancers, 323 hepatocellular carcinoma, 344–345 interferon-α gene expression response, 530–531 ovarian cancer, 342–343 overview, 341 subgroup stratification, 87–88 Docetaxel, Gradishar study, 224–225 Dose animal studies for estimation, 13–15, 18 escalation and Moore schema, 41–43 ICH guidelines, 18 modification for adverse events, 420–423 units expressed in terms of body surface area, 35–36 Dose-limiting toxicity (DLT), 18, 42–43 Double-blind study, 112 Index Double-dummy study, 112 Doxorubicin resistance, 78 toxicity, 74, 76 Drug Amendments Act of 1962, 580 Drug origins animal models, 11–15 antibody drugs, 7–9 cladribine, 5–6 5-fluorouracil, high-throughput screening, methotrexate, 3–4 overview, 1–2 paclitaxel, 4–5 ribavirin, warfarin, 2–3 Drug resistance ABC transporters, 76–77 cross-resistance, 77–78 doxorubicin, 78 exclusion criteria, 76 imatinib, 79–80 paclitaxel, 78–79 tamoxifen, 79 Drug safety, see Adverse events Dupont study, 158 Dy schema, 36–37 E EAE, see Experimental autoimmune encephalitis ECOG performance status, 73–74 EDSS, see Expanded Disability Status Scale Elixir Sulfanilamide, 578–579 EMA, see European Medicines Agency Endpoints clinical endpoint, 191 hematological cancers event-free survival, 311–313 relapse-free interval, 313 hepatitis C trials, see Hepatitis C virus mechanism of action and surrogate endpoints, 473 multiple endpoints, 194–195 multiple sclerosis trials, see Multiple sclerosis Oncology trial endpoints, see Disease-free survival; Objective response; Overall survival; Progression-free survival; Time to progression; Time to distant metastasis Phase I trial, 191 relatively objective endpoints versus relatively subjective endpoints, 193–194 selection, 194–195 start date, 162–163 supportive care and health-related quality of life endpoint conflict, 136 surrogate endpoint, 191–192 EORTC, see European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Erlotinib, 54 European Medicines Agency (EMA) historical perspective, 581–583 safety definitions, 423 European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC), 387 Event-free survival (EFS), 311–313 Exclusion criteria Clinical Study Protocol, 64–65 drug resistance as basis for exclusion, 76 immune status as basis for exclusion, 81 poor performance as basis for exclusion, 73–74 prior therapy, 72–73, 80 run-in period, 57–58 toxicity as basis for exclusion, 74–76 Expanded Disability Status Scale (EDSS), 360–361, 406–407 Experimental autoimmune encephalitis (EAE), 13, 510–511 F FAB classification, see French-American-British classification FACT, see Functional Assessment of Cancer Treatment FDA, see Food and Drug Administration Federal Food, Drug, and Cosmetic Act of 1938, 578–580 Findeisen study, 348–349 Fingolimod, multiple sclerosis management, 366, 507–508 FISH, see Fluorescence in situ hybridization FLIC, see Functional Living Index: Cancer Florescu study, 159 Fluorescence in situ hybridization (FISH), biomarker validation, 336 5-Fluorouracil, origins, Foekens study, 268 629 630 Index Food and Drug Administration (FDA), review process Approval Letter, 603 Clinical Review, 601–602 clinical trial administration process, 592–595 examples, 604–605 meeting types, 600–601 patent intersection, 621–623 Pharmacology Review, 603 Food and Drug Administration Modernization Act of 1997, 580–581 Form E1594, 544–549 Fornoff v Parke Davis, 570 Four-arm clinical trial, 39–41 French-American-British (FAB) classification, hematological cancers, 279–280 Functional Assessment of Cancer Treatment (FACT), 387–388 Functional Living Index: Cancer (FLIC), 387 G Galon study, 339–340 Gastrointestinal stromal tumor (GIST), Demetri’s example of partial response, 203–206 G-CSF, see Granulocyte colony-stimulating factor Geddes study, 149 Geftinib, Maemondo study, 221–224 GenBank, 522–523 Gianni schema, 26 GIST, see Gastrointestinal stromal tumor Glatiramer acetate, multiple sclerosis trial, 408–409 Glioblastoma, supportive/palliative care in placebo arm, 135 GM-CSF, see Granulocyte-macrophage colonystimulating factor Gradishar study, 224–225 Grainick-Strobel study, 160 Gralla study, 161–162 Granulocyte colony-stimulating factor (G-CSF), 28, 438–439 Granulocyte-macrophage colony-stimulating factor (GM-CSF), 28, 438–439 Grever study, 314–315 Grimwald study, 322–323 Grossman study, 409 Guidance for Industry Guidance for Industry E6, 539–540, 551 package insert, 562–564 H Hairy cell leukemia (HCL), 288–289 Hanna schema, 37–38 Hawthorne effect, 132 Hayes study, 255–256 Hazard ratio (HR) calculation, 171–172 overview, 169–170 HCC, see Hepatocellular carcinoma HCL, see Hairy cell leukemia HCV, see Hepatitis C virus Health-related quality of life (HRQoL) adverse event reporting, 458, 460 applications, 385–386 chemotherapy versus surgery decisions, 399 credibility of instruments, 383–384 criteria for instruments in clinical trials, 385 hepatitis C, 411–414 immune disorder instruments multiple sclerosis, 405–409 SF-36, 401–405 oncology instruments breast cancer, 397–398 colorectal cancer, 390–393 formats for disclosing results, 389–390 melanoma, 393–395 non-small cell lung cancer, 396–397 symptoms and functioning, 388–389 types, 387–388 relatively objective endpoints versus relatively subjective endpoints, 193–194 supportive care and health-related quality of life endpoint conflict, 136 timing of questionnaires, 386–387 HED, see Human equivalent dose Hematological cancers, see also specific cancers acute lymphocytic leukemia, 284–287 acute myeloid leukemia, 289–291 acute promyelocytic leukemia, 291–292 chronic lymphocytic leukemia, 287–288 chronic myeloid leukemia, 293–294 classification, 279–282 cytogenetics for diagnosis and prediction, 299–310, 314–315, 323 endpoints in clinical trials event-free survival, 311–313 relapse-free interval, 313 hairy cell leukemia, 288–289 hematopoietic stem cell origins, 282–283 Index leukemic cell localization, 284 minimal residual disease acute lymphocytic leukemia, 317–320 acute myeloid leukemia, 320–322 acute promyelocytic leukemia, 323 detection, 316–317, 320 myelodysplastic syndromes classification, 295–296 clinical features, 294–295 scoring, 296 treatment, 296–299 Hepatitis C virus (HCV) acute versus chronic infection, 370–371 animal models, 12 biomarkers, 381–382 clinical features of infection, 369–370 endpoints in trials Di Bisceglie study, 380–381 McHutchison study, 379–380 epitopes, 520–522 genome, 519 health-related quality of life, 411–414 immune response, 373, 520–531 kinetics of infection, 373–376 protease inhibitor therapy, 518–519 treatment interferon-α, 371 responders versus non-responders, 376–379 ribavirin, 371–372 Hepatocellular carcinoma (HCC) DNA microarray, 344–345 hepatitis C induction, 525 supportive/palliative care in placebo arm, 136 HER2, 25, 225–229, 236, 254, 256, 322, 328, 333–336 Hess v Advanced Cardiovascular Sys., Inc., 620 High-throughput screening, drug discovery, Hoffman-LaRoche, Inc v Promega Corp., 620 Holm study, 166–168 Hoshida study, 344–345 Hosking study, 147–148 HR, see Hazard ratio HRQoL, see Health-related quality of life Human equivalent dose (HED), 15 I ICH, see International Conference on Harmonisation IDS, see Investigational Drug Service IL-12, see Interleukin-12 Imatinib, 37, 79–80 Inclusion criteria Clinical Study Protocol, 64–65 ethical considerations, 82 prior therapy, 72–73 run-in period, 57–58 vaccination as inclusion criterion, 81–82 IND application, see Investigational New Drug application Informed consent, see Consent forms Insert, see Package insert Institutional Review Board (IRB), 441, 541, 594 Intent to treat (ITT) analysis adverse events, 427–431 definition, 143–144 disadvantages, 149–150 drug efficacy analysis, 151–152 inconsistencies, 144–146 modified analysis adverse events, 429–430 exclusion of subjects Berek study, 159–160 Caraceni study, 161 Dupont study, 158 Florescu study, 159 Grainick-Strobel study, 160 Gralla study, 161–162 Kreijkamp-Kaspers study, 160 Leroy study, 157–158 Manegold study, 159 Pinchichero study, 156–157 Vara study, 155–156 Weigelt study, 156 flow chart, 153–155 overview, 152 rationale, 155 non-inferiority trials, 190 per protocol analysis comparison, 146–149 run-in period, 58, 61, 150 start date for endpoints, 162–163 Interactive voice response system (IVRS) applications, 125–126 concerns, 126–128 Interferon-α adverse events, 418–419 gene expression response, 530–531 hepatitis C management, 371, 411–412, 525–526 631 632 Index Interferon-α (Continued ) response, 525–526, 528–531 multiple sclerosis trial, 407–408 Interferon-β, multiple sclerosis management, 408, 508–509 Interferon-γ, hepatitis C virus response, 525–527 Interleukin-12 (IL-12), hepatitis C virus response, 527–528 International Conference on Harmonisation (ICH) adverse event guidelines, 419–420 dose guidelines, 18 endpoint selection guidelines, 194 overview, 583–585 Investigational Drug Service (IDS), 118 Investigational New Drug (IND) application, 586–591 IRB, see Institutional Review Board ITT analysis, see Intent to treat analysis IVRS, see Interactive voice response system Leroy study, 157–158 Leuprolide, 80 Liver cancer, see Hepatocellular carcinoma Llovet study, 240–244 Loi study, 267–268 Lung Cancer Symptom Scale (LCSS), 389 C-reactive protein as biomarker, 347–348 health-related quality of life in non-small cell lung cancer, 396–397 Park study, 239–240 randomization code breaking example, 120 supportive/palliative care in placebo arm, 135 Will Rogers phenomenon non-small cell lung cancer, 107 small cell lung cancer, 107–108 Lymph nodes biology, 93–94 relation between tumors and lymphatic system, 94 sentinal nodes, 96–97 J M Jongen study, 408 MABEL, see Minimal anticipated biological effect level Machin and Gardner study, 172 Maemondo study, 221–224 Magnetic resonance imaging (MRI) multiple sclerosis, 364–367 principles, 362–363 T1-weighted images, 365–326 T2-weighted images, 364–365 Mahr v G.D Searle, 456 Major histocompatibility complex (MHC), classes of molecules, 488–489 Malaria vaccine, randomization code breaking example, 120 Manegold study, 159 Marshall schema, 43–44 Mass spectrometry, biomarker identification, 349 Mathew study, 412–413 Maximum tolerated dose (MTD), 18, 41–43, 54 McDermott study, 244–245 McEwen v Ortho, 572 McHutchison study, 379–380 MDS, see Myelodysplastic syndromes Mechanism of action (MOA) cancer immune response, 493–501 drug combination considerations cross-resistance avoidance, 474–475 synergy, 474 K Kanamycin, package insert and liability protection, 570 Kaplan-Meier plot censoring data, 168–169, 172 Holm study, 166–168 Maemondo study, 224 overview, 165–166 Romond study, 255 Siamon study, 231 time of enrollment and data, 172–174 Van Cutsem study, 232–233 Kappos study, 366–367 Katsumata schema, 31–35 c-Kit, 37, 79 Koelink study, 338–339 KRAS biomarker utilization, 329–330 mutation in colorectal cancer, 232–234 Kreijkamp-Kaspers study, 160 L LCSS, see Lung Cancer Symptom Scale Lenalidomide mechanism of action, 298 myelodysplastic syndrome management, 298 Index expected adverse drug reactions, 473–474 hepatitis C virus genome, 519 immune response, 520–531 protease inhibitor therapy, 518–519 immune component diseases antibody drugs, 486 cell-mediated immunity, 478–482 concept pairs in immunology, 487–490 cytokines, 476–478, 483–484 disease examples, 504–506 immune adjuvant fine-tuning in cancer treatment, 485–486 multiple sclerosis, 507–515 overview, 475–476, 503–504 regulatory T cell inhibitors, 486–487 Toll-like receptor agonists, 484–485 vaccine modulation of immune system, 483 importance of knowledge, 471–472 package insert, 472 surrogate endpoints, 473 MedDRA dictionary, 442–443 Medical writing Case Report Form writing style, 444–445 consent form language level, 543–544, 553–555 package insert ambiguity of writing, 567–568 process formatting issues, 597–600 grammatical issues, 596–597 overview, 595–596 Medicines and Healthcare Products Regulatory Agency (MHRA), historical perspective, 585–586 Meditation training, multiple sclerosis trial, 409 MedWatch form, 448–450 Melanoma C-reactive protein as biomarker, 348–349 health-related quality of life, 393–395 randomization code breaking example, 121 Meropol study, 248–249 Mesothelioma, supportive/palliative care in placebo arm, 135 Metastasis staging, 95–96 subgroup stratification, 84 Methicillin-resistant Staphylococcus aureus (MRSA), 47 Methotrexate, origins, 3–4 MHC, see Major histocompatibility complex MHRA, see Medicines and Healthcare Products Regulatory Agency MicroRNA cancer biology, 269–270 prognostic factor in breast cancer, 268 Minimal anticipated biological effect level (MABEL), 14 Minimal residual disease (MRD) acute lymphocytic leukemia, 317–320 acute myeloid leukemia, 320–322 acute promyelocytic leukemia, 323 detection, 316–317, 320 MOA, see Mechanism of action Moore schema, 41–43 Morris study, 340 MRD, see Minimal residual disease MRI, see Magnetic resonance imaging MRP1, 77 MRSA, see Methicillin-resistant Staphylococcus aureus MS, see Multiple sclerosis MSFC score, see Multiple Sclerosis Functional Composite score MTD, see Maximum tolerated dose Multidrug resistance, see Drug resistance Multiple sclerosis (MS) animal model, 13, 510–511 classification, 355–356 clinical features, 506–507 diagnosis, 356–357 endpoints in clinical trials overview, 357–359 primary endpoint, 359–360 secondary endpoints, 360–361 timing of measurement, 359 health-related quality of life, 405–409 Kappos study, 366–367 lesions, 511–515 magnetic resonance imaging, 364–367 management cladribine, 509–510 fingolimod, 507–508 interferon-β, 508–509 natalizumab, 507 randomization code breaking example, 121–122 Will Rogers phenomenon, 108–109 Multiple Sclerosis Functional Composite (MSFC) score, 360–361 Muss study, 398–399 633 634 Index Myelodysplastic syndromes (MDS) classification, 295–296 clinical features, 294–295 cytogenetics for diagnosis and prediction, 308–310 scoring, 296 treatment, 296–299 N Natalizumab, multiple sclerosis management, 507 Natural killer (NK) cell, 481, 497–499, 527–528 Neoadjuvant therapy adjuvant therapy advantages, see Adjuvant therapy advantages over adjuvant therapy chemotherapy tolerance, 275 micrometastasis killing, 273 organ preservation, 273–274 subsequent therapy decisions, 274–275 surgery and tumor downstaging, 273 overview, 271–272 NK cell, see Natural killer cell No adverse effect dose level (NOAEL), 14 No-treatment arm, 132–133 NOAEL, see No adverse effect dose level Non-inferiority analysis, 187–190, 415 Non-small cell lung cancer, see Lung cancer Norethindrone, package insert and liability protection, 572 Nortvedt study, 407–408 Null hypothesis, 178 O Objective response confusion avoidance cytostatic versus cytotoxic drugs, 210–211 date for beginning measurements, 208–209 multiple measurements, 209–210 rate and percent reporting, 210 standards for objective response, 211 overall survival advantages with cytostatic drug trials, 242–244 overview, 197–198 Paccagnella study of oncology endpoint agreement, 238 partial response examples Demetri’s study, 203–206 van Meerten study, 205–206 progressive disease example, 206–207 Response Evaluation Criteria for Solid Tumors criteria, 198–203 survival correlation studies, 207–208 One-arm clinical trial, 36, 111, 131 One-tailed test, 176–177 Open-label trial, 111–112 Ovarian cancer, DNA microarray, 342–343 Overall survival advantages with cytostatic drug trials, 242–244 disease-free survival comparison, 254–255 overview, 213 Paccagnella study of agreement of oncology endpoints, 238 prognostic biomarker validation, 259–261 progression-free survival comparison Bedikian study, 234–235 Gradishar study, 224–225 Maemondo study, 221–224 overall survival advantages gold standard, 219 trigger date, 219–220 overall survival limitations confusion from effects of non-study drugs given to subjects who leave, 216 ethics, 218 follow-up period required for overall survival data collection, 216 multiplicity of causes of death, 217–218 premature termination of trial, 218 redundancy of conclusions, 218–219 timeframe and weakened conclusions, 217 Robert study, 225–228 Siamon study, 228–230 Van Cutsem study, 230–234 time to distant metastasis comparison, 266–267 Oxytocin, package insert and liability protection, 570–571 P Package insert ambiguity of writing, 567–568 black box warning, 565–567 consent form comparison, 573 drug classes, 565 Guidance for Industry, 562–564 liability protection, 568–573 limitations off-label use, 574–575 standard of care, 573–574 Index mechanism of action, 472 overview, 561–562 Paclitaxel Gradishar study, 224–225 Maemondo study, 221–224 origins, 4–5 resistance, 78–79 Robert study, 226–227 PADER, see Periodic Adverse Drug Experience Report Palliative care, placebo arm, 134–136 Park study, 239–240 Patent documents, 611–612 FDA review process intersection, 621–623 historical perspective, 607–609 law, 619–621 process overview, 609–611 structure background section, 612–613 claims, 613–615 timeline, 616–619 Patient-reported outcomes (PROs) head and neck cancer instrument, 458–459 overview, 457–458 Perez schema, 24–25 Periodic Adverse Drug Experience Report (PADER), 451–452 Periodic Safety Update Report (PSUR), 451–452 Per protocol (PP) analysis drug efficacy analysis, 151–152 intent to treat analysis comparison, 146–149 non-inferiority trials, 189–190 run-in period, 150 start date for endpoints, 162–163 Peterson v Parke Davis, 570 PFS, see Progression-free survival Pharmacokinetics, Marshall schema, 43–44 Pharmacology Review, 603 Phase I trial consent forms, 555–556 dosing, 18 endpoints, 191 FDA regulatory review process, 604–605 goals, 17 Phase II trial FDA regulatory review process, 604–605 overview, 19 Philadelphia chromosome, 285–287, 305–307, 317–318 Pinchichero study, 156–157 Placebo active placebo, 133–134 ethics, 136–141 Hawthorne effect, 132 no-treatment arm, 132–133 overview, 131–132 physical aspects, 133 run-in period, 61 supportive/palliative care in placebo arm, 134–136 Platelet transfusion leukemia, 292 myelodysplastic syndromes, 297 Polio vaccine, package insert and liability protection, 571–572 Population, biostatistics, 174–175 PP analysis, see Per protocol analysis Prior therapy, inclusion/exclusion criteria, 72–73 Progression-free survival (PFS) advantages as endpoint, 215 definition, 214, 237 disease-free survival comparison, 252 overall survival comparison advantages of overall survival gold standard, 219 trigger date, 219–220 Bedikian study, 234–235 Gradishar study, 224–225 limitations of overall survival confusion from effects of non-study drugs given to subjects who leave, 216 ethics, 218 follow-up period required for overall survival data collection, 216 multiplicity of causes of death, 217–218 premature termination of trial, 218 redundancy of conclusions, 218–219 timeframe and weakened conclusions, 217 Maemondo study, 221–224 Robert study, 225–228 Siamon study, 228–230 Van Cutsem study, 230–234 rate, 220 six-month progression-free survival, 220 time to progression comparison, 214, 238–239 PROs, see Patient-reported outcomes 635 636 Index Prostate cancer, Will Rogers phenomenon, 106–107 Prostate-specific antigen (PSA), 80 PSA, see Prostate-specific antigen Psoriasis health-related quality of life, 404 immune response, 505 PSUR, see Periodic Safety Update Report PTEN, 259–261 Puhalla schema, 27 Pure Food and Drug Act of 1906, 577–578 P-value calculation, 166, 168, 180–186 hypothesis testing, 177–180 R Ramlau study, 75 Randomization blocked randomization, 123 Clinical Study Protocol examples of randomization code breaking, 119–122 simple randomization, 114–115 Rate objective response reporting, 210 progression-free survival, 220 RECIST criteria, see Response Evaluation Criteria for Solid Tumors criteria Reck schema, 38–40 Rectal cancer disease-free survival and neoadjuvant versus adjuvant therapy, 257–258 Will Rogers phenomenon, 108 Relapse-free interval (RFI), 313 Resistance, see Drug resistance Response Evaluation Criteria for Solid Tumors (RECIST) criteria, 198–203 RFI, see Relapse-free interval Rheumatoid arthritis, immune response, 505 Ribavirin black box warning, 566 hepatitis C management, 371–372 origins, Ring study, 258–259 Roach study, 266–267 Robert study, 225–228 Roh study, 257–258 Romond study, 254–255 RRM1, 259–261 Rudick study, 406–407 Run-in period adherence and compliance, 56–57 baseline adverse event detection, 52 decision tree, 59–60 desired predetermined response, 58–59 Dy schema, 36–37 Hanna schema, 37–38 inclusion/exclusion criteria, 57–58 intent to treat analysis, 58, 61, 150 lifestyle adjustment, 55 maximum tolerated dose determination, 54 metabolic characteristic homogeneity, 56 overview, 51–52 pain control screening, 53 patient exclusion, 52–53 per protocol analysis, 150 placebo run in, 61 self-control group creation, 60 steady state concentration establishment, 54–55 washout period, 52 S Safety, see Drug safety Sample, biostatistics, 174–175 Schema Baselga schema, 31 Blumenschein schema, 28–30 Czito schema, 30 Dy schema, 36–37 examples, 22–24 Gianni schema, 26 Hanna schema, 37–38 Katsumata schema, 31–35 Marshall schema, 43–44 Moore schema, 41–43 overview, 20–22 Perez schema, 24–25 Puhalla schema, 27 Reck schema, 38–40 Sekine schema, 27 stratification, 68–70 tumors staging, 23, 30 progression, 35, 43 Untch schema, 26–27 Sekine schema, 27 Sentinal nodes, 96–97 Sepsis, randomization code breaking example, 121 Sertraline, black box warning, 567 Index Sethi study, 148 SF-36 immune disorder use, 401–405 oncology, 387, 399 Shepherd study, 395–396 Siamon study, 228–230 Single-blind study, 112 SJS, see Stevens-Johnson syndrome SLE, see Systemic lupus erythematosus Small cell lung cancer, see Lung cancer SNOSE technique, 116–119 Snyder v Hoffman-LaRoche, 456 Sorafenib, 421 Spentzos study, 342–343 Spitzer Quality of Life Index (SQLI), 387 SQLI, see Spitzer Quality of Life Index Staging Blumenschein schema, 28–30 breast cancer ductal carcinoma in situ, 100–101 invasive breast cancer, 101 lobular carcinoma in situ, 100–101 TNM staging, 101–105 colorectal cancer, 97–100 Czito schema, 30 schema, 23, 30 stratification of subjects, 68 tumors, see Tumor Node Metastasis staging Stevens-Johnson syndrome (SJS), 433–435 Stratification, see also Exclusion criteria; Inclusion criteria goals, 66–67 overview, 65–66, 115 schema, 68–70 staging of disease, 68 subgroups age stratification, 83–84 analysis prognostic factor identification, 87 recommendations for specific course of treatment, 86–87 creation caveats, 67–68 defining in Clinical Study Protocol, 82–83 dropping from trial, 88–89 examples, 70–72 FDA approval value, 85–86 gene expression, 87–88 metastasis versus no metastasis, 84 smokers versus non-smokers, 85 Study design, see Clinical Study Protocol; Schema Subgroups, see Stratification Superiority analysis, 187–190 Supportive care health-related quality of life endpoint conflict, 136 placebo arm, 134–136 Surrogate endpoint, 191–192, 473 Systemic lupus erythematosus (SLE), immune response, 505 T Tamoxifen, resistance, 79 T cell activation and maturation, 482 antigen presentation, 479–480, 482 cancer immune response, 494–497 CD4   versus CD8   T cells, 489 classification, 481 hepatitis C virus response, 523–524, 526, 532 multiple sclerosis lesions, 511–513 naive response versus memory response, 489 regulatory T cell cancer immune response, 499–501 inhibitors, 486–487 T helper cell response, 488 tumor infiltrating T cell as colon cancer biomarker, 339–340 TDM, see Time to distant metastasis TEN, see Toxic epidermal necrosis Tenuto v Lederle, 571 Thalidomide, 579 Three-arm clinical trial, 19, 21, 25–26, 47 Thymidine phosphorylase messenger RNA expression and protein levels, 249–250 survival biomarker, 248–249 Time to distant metastasis (TDM) data acquisition, 264–265 median time to distant metastasis, 266 overall survival comparison, 266–267 overview, 263–264 prognostic factors in breast cancer gene array, 267–268 microRNA expression, 268 Time to progression (TTP) advantages patients receiving additional chemotherapy after trial, 239–240 637 638 Index Time to progression (TTP) (Continued ) survival endpoint comparison, 240–242 biomarker validation, 334–335 definition, 237 efficacy demonstration short trial, 245–246 small trial, 244–245 median time, 247 overall survival advantages with cytostatic drug trials, 242–244 Paccagnella study of agreement of oncology endpoints, 238 progression-free survival comparison, 214, 238–239 thymidine phosphorylase as survival biomarker, 248–249 TLRs, see Toll-like receptors TNM staging, see Tumor Node Metastasis staging Toll-like receptors (TLRs) agonists, 484–485, 495 innate immunity, 490 Toxic epidermal necrosis (TEN), 433–434 Trastuzumab, 25, 226–229 t statistic, 180 TTP, see Time to progression Tumor infiltrating T cell, colon cancer biomarker, 339–340 Tumor Node Metastasis (TNM) staging biology lymphatic system, 93–94 relation between tumors and lymphatic system, 94 tumors, 93 breast cancer, 101–105 colorectal cancer, 97–100 historical perspective, 92 metastasis, 95–96 overview, 91–92 revisions, 92–93 sentinal nodes, 96–97 Two-arm clinical trial, 19, 21, 29, 38 Two-tailed test, 176–177 Typhoid vaccine, randomization code breaking example, 120 U Ulcerative colitis C-reactive protein levels, 341 immune response, 506 Unblinding, 112 Untch schema, 26–27 Urothelial tract cancer, supportive/palliative care in placebo arm, 135 V Vaccine malaria vaccine randomization code breaking example, 120 modulation of immune system, 483 polio vaccine package insert and liability protection, 571–572 typhoid vaccine randomization code breaking example, 120 vaccination as inclusion criterion, 81–82 Van Cutsem study, 230–234 van Meerten study, 205–206 Vara study, 155–156 Vascular endothelial growth factor (VEGF), 41, 96 VEGF, see Vascular endothelial growth factor Vogel study, 334–336 Voice-Related Quality of Life (V-RQOL), 387 V-RQOL, see Voice-Related Quality of Life W Wang study, 343–344 Warfarin, origins, 2–3 Watanabe study, 398 Wee study, 264–265 Weigelt study, 156 Will Rogers phenomenon definition, 106 lung cancer non-small cell lung cancer, 107 small cell lung cancer, 107–108 multiple sclerosis, 108–109 prostate cancer, 106–107 rectal cancer, 108 Wong study, 348–349 Writing, see Medical writing WT1, 320–322 Y Yellow Fever Commission, 536–537 Z Z statistic P-value calculation, 180–186 table of areas in tail of standard normal distribution, 186–187 Zwibel study, 408–409 ... study drug; and Three-arm study l l l l a.  Active control Clinical trials typically use an active control treatment, including clinical trials that test new drugs, new surgical methods, and new... Phase III clinical trials The goals of these trials are to acquire data on safety and efficacy, to receive regulatory approval for the relevant drug or medical device, and to provide safe and effective... goals of a Phase I clinical trial are to assess safety and to determine an effective dose suitable for subsequent Phase II trials In clinical trials on anti-cancer drugs, Phase I trials are often