A bried overview on the assimilation of neural network

based solutions by the (bio)pharmaceutical industry

João Almeida Lopes

Faculdade de Farmácia da Universidade de Lisboa ISEP, Porto - 2015.10.01

Summary

Part [i]

Some concepts around the Artificial Neural Networks theme

Part [ii]

Applications of Artificial Neural Networks in the pharmaceutical field (R&D)

Part [iii]

Illustration of the use of Artificial Neural Networks in three situations (pharmaceutical processes)

Machine Learning

Machine learning deals with the construction and study of systems that can learn from data.

A supervised learning algorithm analyzes the training data and produces and inferred function, which can be used for mapping new examples.

In unsupervized learning, no labels are given to the learning algorithm, leaving it on its own to group similar inputs, also called clustering.

Purposes:

• association

• classification (clustering)

• transformation (different representation)

• modelling

Artifical Neural Networks are Machine Learning Models

Adapted from Demuth & Beale (2004) Neural Network Toolbox For Use with MATLAB®, The Mathworks, Natick, MA

Artificial Neural Networks (ANN)

Artificial neural networks (ANN) are computational models inspired from the nature of neurons.

Neurons are inter-connected and arranged in layers.

Can be of very different nature.

The number of parameters (degrees of freedom) is normally quite high.

Essentially a “black-box” model fit for a purpose but difficult to interpret.

Normally, ANNs have problems to generalise as is the case with other data driven modelling approaches.

Wesolowski & Suchacz (2012) Artificial Neural Networks: theoretical background and pharmaceutical applications: a review, J. AOAC Internat., 95(3) 652-668(17)

ANNs can be of different nature and serve different purposes

Perceptron Linear Filter Feedforward Elman (dynamic) Hopfield (dynamic)

Self Organizing Map (Kohonen) Learning Vector Quantization (hybrid) Radial Basis

Adapted from Demuth & Beale (2004) Neural Network Toolbox For Use with MATLAB®, The Mathworks, Natick, MA

Pre

dic

tion

Cla

ssific

atio

n

ANNs’ issues

To discover the best ANN model, numerous candidate models must be trained and evaluated. Problem is that there are a number of choices.

• Network type (feedforward, Kohonen, Elman, etc.)

• Network topology/size/structure (static, dynamic, etc.)

• Learning algorithm

• Learning procedure (batch, incremental, etc.)

• Learning algorithm parameters (learning rate, regularization, etc.)

• Fitting function

• Stopping procedure/validation

Special precautions are to be taken to avoid erroneous conclusions.

• Network initialization

• Over-fitting

• Model selection

Verikas & Bacauskiene (2003) Using artificial neural networks for process and system modelling, Chemom. Intell. Lab. Syst. ,67,187– 191

ANNs don’t do miracles

ANNs estimate patterns or relationships (when properly used) directly from data. Therefore, ANNs don’t do miracles*!!

If the data is of bad quality, ANNs will generate spurious relationships and these must be detected.

One of the above profiles is a near infrared spectrum taken from a pharmaceutical product (powder mix). Which one is it?

*Incorporation of additional knowledge might be coupled with ANNs and these models are generally called “hybrid models”.

Rat

e

Diffu

se R

eflect

ance

Wavelength Time

Euribor 6 months rate NIR spectrum

Number of publications involving “neural networks” in the pharma field Source: Web of Science (Thompson, Reuters)

Note (search strings):

TS=(neural networks) AND PY=####

TS=(neural networks) AND TS=(drug discovery OR pharmaceutics OR pharmaceutical industry OR pharmaceutical OR pharmaceutical technology OR drug design OR drug synthesis OR drug production) AND PY=####

Pharma encompasses circa 0.7% of all ANN related publications

ANNs applied to pharmaceutical sciences [i]

DRUG DESIGN & DRUG DISCOVERY

• Analysis of structure–activity data (analysis of multi-dimensional data)

• Establishment of quantitative structure–activity relationships (QSAR)

• Combinatorial discovery

• Toxicity modelling

• Gene prediction

• Locating protein-coding regions in DNA sequences

• 3D structure alignment

• Pharmacophore perception

• Docking of ligands to receptors

• Automated generation of small organic compounds

• Design of combinatorial libraries

Terfloth & Gasteiger (2001) Neural networks and genetic algorithms in drug design, Drug Discov. Today, 6(15), 102-108

Winkler (2004) Neural networks as robust tools in drug lead discovery and development, Mol. Biotechnol. 27(2),139-68

Livingstone & Salt (1992) Regression analysis for QSAR using neural networks. Bioorg. Med. Chem. Lett. 2, 213–218

ANNs applied to pharmaceutical sciences [ii]

DRUG DELIVERY

• Structure Retention Relationship (SRR) methodology in pharmacological research

• Pharmacokinetics and pharmacodynamics modelling

• Preformulation

• Optimization of pharmaceutical formulations

• Quantitative Structure-Activity Relationships (QSAR) and Quantitative Structure-Property Relationship (QSPR)

• In Vitro In Vivo correlations

• Proteomics and genomics

• Diagnosis of disease

Sutariyaa et al. (2013) Artificial Neural Network in Drug Delivery and Pharmaceutical Research, The Open Bioinform J., 7, 49-62

Ibrić et al. (2003) Artificial neural networks in the modeling and optimization of aspirin extended release tablets with eudragit L 100 as matrix substance, AAPS PharmSciTech., 4(1), 62–70.

Bourquin et al. (1997) Basic concepts of artificial neural networks (ANN) modeling in the application to pharmaceutical development, Pharm. Dev. Technol., 2(2), 95-109

Takayama et al. (1999) Artificial Neural Network as a Novel Method to Optimize Pharmaceutical Formulations, Pharm. Res.,16(1), 1-6

ANNs applied to pharmaceutical sciences [iii]

PHARMACEUTICAL TECHNOLOGY/PRODUCTION

• Drugs production: synthesis (biological, chemical, etc.)

• Drugs production: downstream operations (isolation, purification,etc.)

• Processes modelling (granulation, coating, tabletting, mixture, etc.)

Mendyk et al. (2015) From Heuristic to Mathematical Modeling of Drugs Dissolution Profiles: Application of Artificial Neural Networks and Genetic ProgrammingComputational and Mathematical Methods in Medicine, Article ID 863874, 9 pp.

1 1 2

Examples

Case [i] Micro scale (research & development)

Predicting properties of polymeric nanoparticles for cancer vaccines manufacturing

Case [ii] Laboratory scale (polymers manufacturing)

Monitoring a laboratory scale bioreactor for polyhydroxybutyrates production

Case [iii] Industrial scale (antibiotics manufacturing)

Modelling an industrial scale fermentation process for antibiotic production

Case [i] Predicting properties of polymeric nanoparticles for cancer vaccines manufacturing Objective:

to predict the properties of polymeric nanoparticles (average size around 150nm) to be used as cancer vaccine from manufacturing parameters.

INPUTS: Manufacturing parameters

(PVA, Glycol Chitosan, Surfactant)

OUTPUTS: Nanoparticles properties

(Size, Zeta Potential, Polydispersity Index,

Encapsulation Efficiency, Loading Capacity)

Feedforward ANN

Case [i] Experimental design (22 formulations)

Case [i] Manufacturing conditions impact on nanoparticles properties

Influence of input parameters on the estimation of particles average size (Z-average)

Influence of PVA and Glycol Chitosan Influence of surfactants

(external phase)

Case [ii] Monitoring a laboratory scale bioreactor for polyhydroxybutyrates (PHB) production

Case [ii] The process was monitored in-situ and in real-time with near infrared spectroscopy

batches: 4 batch cycles (A,B,C,D)

On-line variables:

Time (h)

O2(%)

pH

NIR spectra (900-1700 nm)

Off-line variables:

Ammonia (N-mmol.L-1)

PHB (C-mmol.L-1)

PHV (C-mmol.L-1)

Biomass (C-mmol.L-1)

Near infrared spectroscopy (NIR)

Feedforward ANN

Case [ii] NIR-based ANN model predictions

Experimental [PHB] (C-mmol.L-1)

AN

N p

redic

ted [

PH

B]

(C-m

mo

l.L-1)

Test batch

R2>0.9

Near infrared spectra

Raw

Processed

Predictions of [PHB]

Case [iii] Modelling an industrial scale fermentation process for antibiotic production

Fermentation Raw-materials

Inoculum

Case [iii] Fermentations are intrinsically complex processes

A. simplification of the major metabolic pathways in a cell

B. non-localized biosynthesis in industrial strains

C. filamentous antibiotic-producing microorganisms in submerged culture

D. diffusional limitations inside pellets add to the biochemical complexity

E. antibiotic production takes several days in large fed-batch stirred tank reactors

antibiotic

secretion

intr

ace

llu

lar CELL WALL

IPNS

IPN

CYS VAL

ACVS

-AAA a

AT PhCO~CoA

PenG

nutrients uptake

organelles 0.2-10 mm microbial

filament

50 mm

microbial

pellet

1 mm

fermentation tank 100 m3

the cell factory

Case [iii] Fermentation process description

• Stagewise processes (scale-up of inoculum)

Inoculum (1.5 days) Fermentation (10 days)

• Slow non-linear time varying processes

• History dependent processes

• Natural variability of complex raw materials

• Consistent control hampered by monitoring and many intrinsic difficulties of bioprocesses

• Biomass growth in pellets

Inefficient operation control

Selection

Lyophilized

spores

Agar

culture

Vegetative

culture

Inoculum

Fermentation

(glucose syrup, phenylacetic acid,animal and

vegetable oils, ammonia and ammonium

sulphate)

Additions

Case [iii] Objectives

Oxygen (air)

Substrates

Precursor

Biomass

Product

(antibiotic)

• On-Line (Feed Rates, O2, CO2,Pressure, pH, Temperature, Air Flow, Derived Process Variables)

• Off-Line (Biomass, Substrates and Product Concentrations, Physical Parameters)

Case [iii] Major challenges

To model a complex system with almost no knowledge about process kinetics.

Industrial fermentation process with complex raw materials (variable composition) and instrinsic variability caused by bacteria.

Process with a total duration of approximately 140 hours.

Many operating variables that are changed over time according to an established policy.

Historical data not appropriate to capture process dynamics (too many similar batches) with lots of missing data.

Available neural network types not ideal to model batch processes.

Limited number of experimentally designed batches (industrial scale tests).

Case [iii] Solutions

Experimental design of a series of 6 fermentations at industrial scale with variations in all input process variables (using a genetic algorithm).

Runnning all 6 experiments and collecting the maximum amount of data with more frequency during the first 24 process hours.

Adapt feedforward ANNs to handle batch data (new training algorithm and topology).

Implementing the predictive ANNs and integrating predictions in the process monitoring console (Gensym G2 software).

Case [iii] “Static” versus “Recurrent” ANNs

Recurrent ANN Standard ANN

Outputs are used as

inputs at the next

prediction step

A simple recurrence rule

Case [iii] “Static” versus “Recurrent” ANNs

Feedforward ANN (“static”) Feedforward ANN (“recurrent”)

Case [iii] ANNs predictions

Bio

mass

(g

.L-1

)

AP

I C

on

c. (a

.u.)

Pre

cu

rso

r C

on

c. (

g.L

-1)

Bio

mass

(g.L

-1)

Pre

cu

rso

r C

on

c. (

g.L

-1)

AP

I C

on

c. (a

.u.)

ANN inputs are state variables (typically

concentrations), off-gas measurements, physical

variables (pH, temperature), feedrates and aeration

Dynamic/Recurrent ANN topology

Test Batch A Test Batch B

Solid lines are ANN estimations and markers are experimentally observed data

Time (h) Time (h)

Case [iii] Managing ANNs use and ANNs’ estimations

A small set of rules used essentially to detect different process stages

Case [iii] Monitoring console

Feed rates, volume and tank concentrations monitoring

Fermenter

attributes 10 different tanks can be monitored

An instance of

an animated

fermenter

object

Locates the last

saved data file

related to the

chosen tank

On-Line monitored data

Network prediction