M&S in early development (to support FTiM)

Modelling and simulation support for design of First-in- Man studies: the MEL approach Hélène Karcher, Stacey Tannenbaum, Philip Lowe Modelling & Simulation, Novartis Pharma G EM-EFPI Workshop on the role and scope of modelling and simulation in drug development 3 th November st December, 2 2 M&S in early development (to support FTiM) M&S to support design of First-in-Man studies the MEL approach Utilising prior information (in-vitro, pre-clinical and literature) iomarker role in chain of causal evidence nimal (in-vitro & in-vivo) studies Defining PK-PD strategy M&S should design safe studies to achieve, efficiently, desired goal, whether it be healthy volunteer safety and tolerability PK and PD safety assessment plus clinical benefit highlight uncertainties in whatever model(s) is (are) chosen may not always be popular with our project teams, but a key point in design-test cycle 2 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2

3 bstract for examples s with clinical drug development, preclinical development also has phases: I initial in vivo pharmacology II non-glp dose range finding III GLP toxicology Together with an array of in vitro experiments comparing species, these enable an integrated safety assessment prior to entry into man, documenting to investigators and authorities the minimum acceptable biological effect level (MEL) for a first dose in man pharmacokinetic-pharmacodynamically drug-target binding guided process to ascertain the MEL will be exemplified. 3 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 4 effective -Sep-7 4 OST -25 Philip Lowe 27-May-9 usiness Use Only 2

5 eyond the safety factor approach From NOEL to MEL, minimal EPTLE biological effect level for a first dose in a human Target-related PD effect % Toxicological effect % What level of effect is acceptable with a first dose in human? nticipated PD Nonhuman PD nticipated Toxicology Toxicology Most-sensitive animal species No observable adverse effects MEL First dose in human NOEL nimal NOEL Dose or Exposure MEL: Minimal EPTLE iological Effect Level NOEL: No Observable dverse Effect Level 5 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 6 How? process for scaling drug effects from non-human species to man 2 3 Non-human dose-response nticipated dose-response Measure exposure in same experiments Predict exposure Link with effects Non-human exposure -response djust for interspecies differences or assume same nticipated exposureresponse... rather than assuming that a mg/kg bodyweight dose in pharmacology or toxicology species will give equivalent effects in man 6 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 3

7 How? concept document causal chain from drug exposure to clinically measureable effects Drug absorbed binds changing Drug in body Target(s) iomarkers cleared causing dverse effects eneficial clinical effects Investigate, for each step in the causal chain interspecies differences (and, note, different departments often provide the data!) 7 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 8 Example of correlative review of non-human and human (predicted) exposure and toxicity 3 Dog NOEL Dog 46 Extended pharmacology studies provide initial exploration of NOEL U (µg.h/ml). Rat NOEL Rodent HED hpd(s) MRSD 46 42 4.2 ave (ng/ml) orrelate exposure (U, ave ) with NOEL or LOEL through simple exposure-response graphics or function. Dose (mg/kg) ccount and correct for interspecies differences such as unbound fraction, receptor potency, etc. Novartis, ancient data on file 8 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 4

9 Exposure safety envelope over time and start to consider occupancy or free target suppression in safety assessment Serum concentration Pharmacokinetics generate large exposure safety margin for potential off target effects Limit exposure through study design cynomolgus (observed) 3, 3, mg/kg } fold man (first predicted, then actual).,.3, mg/kg Pharmacodynamics target ligand suppression on target ell surface target: iological response, receptor occupancy and/or PK nonlinearity Soluble target: measure total ligand or mb-ligand complex mb-ligand binding model used to predict suppression of free ligand Time (days) 9 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 inding characteristics. iotherapeutic and target related by simple equilibrium reaction Drug Target Drug-Target omplex dd drug, mass balance pushes reaction to the right Phil s 3 rd year notes, 977 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 5

Dose for first clinical study? MEL? What is occupancy at early times, before significant distribution and clearance? Quick method If [mb] >> [target], : binding, use Occupancy = K [ mb] [ mb] D inding affinity.88 nm (TGN42). mg/kg (7 mg) in 2.5 L plasma Peak Occupancy 9.9% With Expression 5 D28 receptors per T cell.3 9 T lymphocytes/litre blood ssume : binding & fast equilibrium Peak Occupancy 9.6% @. mg/kg or % @.5 µg/kg (.µg/kg quick method) EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 Microsoft Excel Worksheet mb Target Target-mb omplex [ mb][ Target] K D = [ omplex] TD = mb omplex mb = TD omplex TT = Target omplex Target = TT omplex 2 ( Kd TD TT ) ( Kd TD TT ) 4. TD. TT omplex = 2 omplex Occupancy = TT 2 Incorporating dynamics of drug distribution and elimination plus target turnover a PKPD binding model iv dose target production or expression mb target mb target complex elimination mb slow clearance V ~ 3 L central, for 7 kg t ½ ~ 3-4 w elimination target general rule: faster than a mb for most (not all) proteins elimination mb target complex general rule: soluble target slow cell surface target faster Mager DR, Jusko WJ. General Pharmacokinetic Model for Drugs Exhibiting Target-Mediated Drug Disposition. J PKPD 2; 28: 57-532 2 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 Ng M, Stefanich E, nand S, Fielder PJ, Vaickus L. Pharmacokinetics/pharmacodynamics of nondepleting anti-d4 monoclonal antibody (TRX) in healthy human volunteers. Pharm Res 26; 23:95-3 6

3 Three components of the PKPD model Pharmacokinetics of the monoclonal antibody: species differences often well understood and easily characterised good prediction to man, either by scaling &/or by reference to prior similar antibodies inding affinity to the target ligand: species differences understood during in vitro characterisation of the drug Localisation, expression and turnover of target, clearance of drug-target complex species differences sometimes not well understood 3 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 4 Monoclonal antibody binds and occupies cell surface target Nonlinear PK correlates with target saturation; need to assess target expression and replenishment; level of receptor or target can vary between species and between diseases ynomolgus monkey, chronic lymphoid leukaemia & multiple myeloma patients PK PD Target expression consistent between cynomolgus monkeys s vary by more than order of magnitude More target higher dose to achieve saturation Model used to advise on dose and regimen for next cancer indication 4 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 7

5 Target binding model enables direct display of occupancy alculated from quantities of mb-target complex and total target djust parameters to selected species using in vitro and in vivo data Monoclonal (µg/ml) Pharmacokinetics (free drug) e4.. -2 mg/kg 2 mg/kg 4 6 8 Time (days) mg/kg DDT(T) = -F*L/V -PLX*L/V -PS*(F/VP/VP) DDT(T) = RIN -F*L/V -PLX*L/V DDT(P) = PS*(F/V-P/VP) PLX=((KD*VTT)-((KD*VTT)**2-4*T*T)**.5)/2 F = T-PLX ;FREE = TOTL - OMPLEX F = T-PLX ;FREE = TOTL - OMPLEX ST = *PLX/T 2 4 Percent saturation Saturation, or target occupancy 9 8 7 6 5 4 3 2-2 2 4 6 8 Time (days) 2 4 6 learances (drug, target, complex ) Volumes (central, peripheral) Rate of target production or expression (R IN, ) inding constant, K D (or K M, I 5, E 5 ) 5 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 6 Scaled suppression of target free ligand to man assuming in vitro binding affinity oncentration (ng/ml) Prior model for mb in human used for human simulations, only adjusting K d to new value Specifies both monoclonal antibody clearance and distribution and target production and turnover Indicate uncertainty in predictions through e.g. Monte-arlo simulation -28. mg/kg.3 mg/kg mg/kg 3 mg/kg -4 4 28 42 mg/kg oncentration (ng/ml) 56 7 84 98 2 Time (days) 26 4 54 68 Monte-arlo simulation 82 MEL -28-4 4 28 42 56 7 84 98 Time (days) 2 26 4 54 68 82 6 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 8

7 Taxonomy of drug-target binding physiological localisations inding model Drug Target omplex Target in solution Target on cells in plasma in tissue interstitium and plasma Mobile cells ells fixed in tissues in blood in tissue interstitium and blood 7 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 8 Summary to scale doses of biologics for man Understand mechanism of action and pharmacology, the location, expression and turnover of the target Delve into limitations of preclinical data for predicting human safety Translate the science to humans; account for differences in relative binding potency, expression and turnover Estimate the clinical starting dose for first time in human study using all pertinent studies, toxicology ND pharmacology not just tox! No simple algorithm for use of MEL case by case think and justify! If necessary use PK/PD data from initial and subsequent dose cohorts to aid dose escalation within first human study onsider stopping rules, exposure limitations Design the right clinical study to mitigate risk 8 EM-EFPI London Karcher & Lowe 3 th Nov- st Dec 2 9