the development of biophysical screening methods to ... · development of an antibody are...

Company Confidential 2012 © Eli Lilly and Company 1

Bryan Jones, Ph.D. Sr. Research Advisor BioTechnology Discovery Research Lilly Biotechnology Center

The development of

biophysical screening

methods to identify better

candidates

Eli Lilly and Company 2 Lilly Biotechnology

What do I mean by “better?”

Many issues that hinder or prevent

development of an antibody are

fundamentally biochemical properties

of the antibody itself:

• Chemical properties – e.g. chemical

degradation

• Physical properties – e.g. solubility,

physical stability, viscosity

• Binding properties: selectivity (or lack

thereof), self-association, “stickiness”

All of which can impact the ability to

manufacture, formulate, and

deliver a safe and efficacious drug

for the patient

The question then becomes:

Can we identify problem

molecules sufficiently early

in the process such that they

can either be avoided, or

“fixed?”

And if so, how?

Molecules that possess not only the appropriate biology, but also appropriate biochemical and biophysical properties that are suitable for manufacture, formulation, and ultimately delivery to the patient


Diverse sources of antibodies

Ideally, we should develop processes that can effectively identify the best candidates for clinical development: • Provides appropriate biological and biochemical characterization • Take full advantage of the available diversity

• Discovery • Humanization

• Biological char. • Engineering


• Discovery• Humanization

• Biological char.• Engineering

From a biochemistry perspective,

diversity isn’t always a good thing…

1000s 1 # of Molecules

100s of mgs to grams

µgs Amount of material

The challenge becomes how to provide useful information about the biochemical & biophysical properties of molecules that impact large quantities and high concentration

Computational approaches

Higher-throughput measurement of useful/relevant

parameters High-throughput, small-scale, predictive

approaches

…without having either available.


In Silico “characterization” screening

Novartis & MIT

Pfizer

Pharm Res. 2014 May

Concentration dependent viscosity of monoclonal antibody solutions: explaining experimental behavior

in terms of molecular properties.

PNAS 2014 Nov 17

In silico selection of therapeutic antibodies for development: Viscosity, clearance, and chemical

stability

Genentech

J Pharm Sci. 2012 Jan

Developability index: a rapid in silico tool for the screening of antibody aggregation propensity

MABs 2015 March

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Lonza Biologics

MABs 2015

Computational tool for the early screening of monoclonal antibodies for their viscosities

MIT & Novartis/Pfizer/MedImmune)

Recently published examples of computational approaches

• Material-free (but computing intensive) • Can be an “initial filter” • Only requires sequences • Automated processing

Batch submission for modeling: fasta files of Vh, Vk

Structure preparation

Rank and predict “developability” based on several key parameters

(SAP, charge, dipole, etc)

Calculate SAP, other biochemical/biophysical

properties

Generate database with properties

Throughput: 10’s-100’s of mabs/day


Computational approaches aren’t ideal,

but they help…

• Some things are easier to predict

than others (e.g. total charge, vs.

charged surfaces), but generally

can identify poor-behaving outliers

• Our experience using published

methods has been variable,

possibly because of:

1. Limited / unique training sets used for

model development

2. Commonly occurring differences in

molecular attributes (e.g. Fc isotype)

Therefore build model(s) trained by our data (knowledge/experience), fit to our

needs and processes, and using a two pronged modeling approach (MOA-based &

machine training)

PK issues related with Non-specific binding (e.g. Genentech

publication)

HMW formation prediction based

upon DI calculation


Extracting biochemical information from

small-scale, unpurified samples

• Quick, automated processing of samples

• Ability to retain affinity purified (neutralized) material for further testing

• Obtain titer, purified material and analytical data from 450ul of CCM without

analyst intervention. ~48 samples per 24 hours

• Sample compartment is cooled and covered to maintain stability of

collected, purified sample fractions.

• Ability to use vials or 96 well plate formats

• Multiple methods used:

• ProtA/G + aSEC (BEH200) + fraction collection/neutralization

• ProtA/G + RP (C18) of collected, reduced fractions

• ProtA/G + aHIC + fraction collection/neutralization (for MS analysis)

CCM

Collected Fractions

Protein G Calculate titer

1st Dimension Protein G

CCM from small scale transient transfections

(2-4 mL)

2nd Dimension RP

Thermo-fisher / Dionex

3.37 4.00 4.50 5.00 5.50 6.00 6.50 7.00 7.50 8.00 8.50 9.00 9.50 10.12

-1.5

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

55.0

60.0

65.0

70.0

75.0

80.0

85.0

93.2mAU

min

2423222120191817161514131211109876543211 - 4.448

2 - 5.494

3 - 6.462

WVL:280 nm

aSEC example - can extract: • % HMW • Retention time & peak width


What should we do when we have

purified protein?

Primary goal is to have a general understanding of the biophysical properties of Mabs/BisMabs (using predictive assays) under both process and formulation conditions before beginning the

development of either…

Practical “Formulation” design

space

Practical “Purification

process” design space

Other conditions of interest for

comparative purposes

Complete “Biophysical Profile”

of the molecule

Meta-analysis and continuous improvement


Probes of protein conformational stability Type of measurement Measured parameter Experimental method

Equilibrium thermal unfolding Thermal unfolding mid-point, Tm Fluorescence, differential scanning calorimetry (DSC)

Equilibrium denaturant unfolding Denaturant unfolding midpoint, D1/2 Fluorescence,

circular dichroism (CD)

Time-dependent thermal unfolding Thermal unfolding rate(s), KT unfolding Fluorescence, CD

Time-dependent denaturant unfolding Denaturant unfolding rates, KD unfolding Fluorescence, CD

Probes of protein colloidal stability Type of measurement Measured parameter Experimental method

Protein-protein interaction Second virial coefficient, A2 Static light scattering, self-interaction chromatography

Protein solubility by precipitation Precipitation midpoint, [precipitant] Ammonium sulfate or polyethylene-glycol precipitation with

static light scattering, turbidity

Protein diffusion interaction Diffusion interaction parameter, kD Dynamic light scattering

Probes of combined colloidal and conformational stability Type of measurement Measured parameter Experimental method

Thermal scanning aggregation Aggregation onset temp., temperature at

which aggregation rate exceeds a certain

value

Static light scattering, size-exclusion chromatography–high-

performance liquid chromatography (SEC–HPLC)

Time-dependent, thermally induced

aggregation

Aggregation rate(s) Static light scattering, turbidity, SEC–HPLC

Table 1: Measurements with the potential to predict long-term storage stability (Adapted from http://www.pharmtech.com/print/200434?page=full)

And by what methods?

Which of these techniques possess the right attributes? • Do not require large quantities of protein • Suitable for automation (simultaneous/nearly simultaneous

characterization of multiple proteins) • Behavior at low concentration that is predictive of high-concentration

behavior


MabA

MabB

[(NH4)2SO4]

pH

Accelerated precipitation & physical

stability

Variables that drive saturation:

Precipitants: PEGs, Ammonium Sulfate

Buffers: pH range (3-10)

Salts: NaCl

Temperatures: 4, 25 C

10

Gibson, et al., J. Pharm Sci 2011


Fully automated solubility ranking

Eight buffer conditions (varying buffer type, pH, [Salt])

PEG 4K as precipitant (one antibody per 96w plate)

Sample requirement = 2.5-3.0mg (@1mg/mL)

• Automated liquid handling (Biomek FX) to transfer Mabs, dilute into

buffer/PEG solutions, transfer to 384 well plate

• Turbidity (A350) measured

1 0 2 0 3 0

0

2

4

6

8

L A 4 9 8

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 3 0 7

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 4 7 4

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

A M E 1 3 3

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 4 8 0

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 4 8 8

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 2 9 4

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

1 0 2 0 3 0

0

2

4

6

8

L A 4 4 3

% P E G 4 K

A3

50

A 5

C 6

C 6N

H 6

P 7 .4

A 5 + S O 4

A 5 + N a C l

B 8 .1

Automated data analysis

LA307 LA474 LA426 LA488 LA480 LA424 LA475 LA463 LA294 LA443 AME133 LA498

B1 (A5) 26 26 20 26 26 26 26 14 26 26 26 26

B2 (C6) 10 4 14 18 14 14 10 14 16 14 18 16

B3 (C6N) 20 12 20 24 18 18 16 22 20 20 24 22

B4 (H6) 12 18 6 26 18 22 20 6 26 26 26 20

B6 (P7.4N) 18 10 18 24 18 18 16 22 18 18 24 20

B10 (NaOAc/SO4, pH5) 26 26 22 20 22 16 14 12 20 26 26 26

B11 (NaOAc/0.2M NaCl pH5) 20 18 18 26 22 20 18 18 18 26 26 20

B12 (Bicine/NaCl pH8.1) 6 6 12 18 10 10 10 16 14 10 20 16

Bu

ffe

r

Mab

Mab 1 Mab 2 Mab 3

Mab 4 Mab 5 Mab 6

Mab 7 Mab 8 Mab 9


Self-association by light-scattering

3.0E-07

3.5E-07

4.0E-07

4.5E-07

5.0E-07

5.5E-07

6.0E-07

6.5E-07

0 2 4 6 8 10 12Dif

fusi

on

Co

effi

cie

nt

(cm

2/s

)

Antibody Concentration (mg/mL)

Interaction parameter (Kdiff), measured by DLS, has been extensively used to predict high concentration behavior of antibodies (e.g. viscosity, solubility)

100 mg/mL, 1 wk, 4 °C


Automated DLS/Kdiff Determination

• 5-point dilutions (in duplicates) ,

requires 4 mg Mab per buffer

condition

• 96w source plate, dilutions

prepared in 384w plate

• Adds mineral oil overlay

Buffer LA307 LA474 LA426 LA488 LA480 LA424 LA475 LA463 LA294 LA443

B1 (A5) -34.4 -30.0 -5.8 -1.0 -0.5 -10.0 19.2 -21.5 14.2 29.8

B2 (C6) -22.7 -15.0 -17.8 -20.5 -5.4 -21.7 -15.3 13.3

B3 (C6N) -23.9 -56.7 -14.1 -7.0 -10.5 -10.5 -8.1 -5.8 -9.8 -0.1

B4 (H6) -42.5 -20.4 -14.5 -21.8 -23.3 7.8 14.1 9.1

B5 (H6N) -25.1 -45.1 -15.2 -8.0 -9.7 -14.1 -6.4 -7.2 -7.8 -2.7

B6 (P7.4N) -26.3 -35.5 -16.8 -7.9 -11.1 -7.9 -13.9 -0.6 -10.1 -4.7

B7 (NaOAc/Cit pH3.3) 16.5 34.7 32.2 39.5 36.7 35.5 37.6 22.3 36.0 31.5

B8 (NaOAc/Cit/Tris pH5) -36.3 -53.1 -10.2 -13.8 -13.2 -13.5 -11.5 -14.0 -10.2

B9 (NaOAc/Cit/Tris pH8) -66.7 -10.1 -12.9 -8.0 -14.3 -1.3 -9.2 -15.3

B10 (NaOAc/SO4, pH5) -26.1 -31.3 -9.5 -9.4 -6.1 -4.4 -9.5 -2.7 -5.0

B11 (NaOAc/0.2M NaCl pH5) -32.0 -33.8 -15.4 -5.0 -4.6 -6.3 -4.2 -9.6 -7.9 -2.7

B12 (Bicine/NaCl pH8.1) -10.9 -18.0 -11.5 -18.7 -11.7 -11.3 -16.1

Bu

ffe

r

Mab


Moving towards a screening-based

approach to candidate identification

• Collectively, these tools

allow for the ID /

elimination of poor-

behaving Mabs before

investing significant effort

in their generation &

characterization

• These tools are sufficiently

high-through put to serve

as engineering screens

when needed

Drug

candidate

100’s

10’s

Few

Computational, 2D-chromatography

Solubility screen Self-association

# of molecules

Rigorous solubility & stability assessment

Mab identification

Antigen binding, cross reactivity

Cell-based activity

In vivo assessment (PK, disease models)

Simultaneous characterization of biological & chemical properties


Many Thanks to:

My entire group – AME Protein BioSciences

Especially:

• Qing Chai (“µ-Drop,” computational methods)

• Samantha Phan, Denisa Foster (2D-

chromatography)

• Jim Shih (automated methods for screening)

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