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Idea to IND

Empowering Global Biotherapeutic Innovation

Antibody Discovery Solution

A one-stop solution for therapeutic antibody discovery, engineering and development



Given a graph and a source vertex in
the graph, find shortest paths from
source to all vertices in the given
graph. Dijkstra’s algorithm is very
similar to Prim’s algorithm for minimum
spanning tree. Like Prim’s MST, we
generate a SPT (shortest path tree)
with given source as root.
We maintain two sets, one set contains
vertices included in shortest path tree,
other set includes vertices not yet
included in shortest path tree. At every
step of the algorithm, we find a vertex
which is in the other set (set of not yet
included) and has a minimum distance from
the source. Below are the detailed steps
used in Dijkstra’s algorithm to find the
shortest path from a single source vertex
to all other vertices in the given graph.
Algorithm Create a set sptSet (shortest
path tree set) that keeps track of vertices
included in shortest path tree, i.e., whose
minimum distance from source is calculated
and finalized. Initially, this set is empty.
Assign a distance value to all vertices in
the input graph. Initialize all distance
values as INFINITE.
Assign distance value as 0 for the source
vertex so that it is picked first. While
sptSet doesn’t include all vertices Pick a
vertex u which is not there in sptSet and
has minimum distance value. Include u to
sptSet. Update distance value of all adjacent
vertices of u. To update the distance values,
iterate through all adjacent vertices. For
every adjacent vertex v, if sum of distance
value of u (from source) and weight of edge
u-v, is less than the distance value of v,
then update the distance value of v.

Given a graph and a source vertex in the
graph, find shortest paths from source to
all vertices in the given graph. Dijkstra’s
algorithm is very similar to Prim’s
algorithm for minimum spanning tree.
Like Prim’s MST, we generate a SPT (shortest
path tree) with given source as root.
We maintain two sets, one set contains
vertices included in shortest path tree,
other set includes vertices not yet included
in shortest path tree.
At every step of the algorithm, we find a
vertex which is in the other set (set of not
yet included) and has a minimum distance
from the source.

Below are the detailed steps used in
Dijkstra’s algorithm to find the shortest
path from a single source vertex to all other
vertices in the given graph. Algorithm
Create a set sptSet (shortest path tree set)
that keeps track of vertices included in
shortest path tree, i.e., whose minimum
distance from source is calculated and
finalized. Initially, this set is empty. Assign
a distance value to all vertices in the input
graph. Initialize all distance values as INFINITE.
Assign distance value as 0 for the source
vertex so that it is picked first. While sptSet
doesn’t include all vertices Pick a vertex u
which is not there in sptSet and has minimum
distance value. Include u to sptSet.
Update distance value of all adjacent vertices
of u. To update the distance values, iterate
through all adjacent vertices. For every
adjacent vertex v, if sum of distance value
of u (from source) and weight of edge u-v,
is less than the distance value of v, then
update the distance value of v.


IDEA

Immunization

• Protein

• Cell Line 

• DNA

mRNA 

𝙄𝙣 𝙑𝙞𝙩𝙧𝙤

• Functional characterization

• Binding/Affinity/Specificity

• Antibody production

• In vitro functional assay

• Developability

Engineering

• Affinity maturation

• Humanization 

• Fc-Engineering 

• Structure-Based Protein Design  

Antigen Design

• Target assessment 

• Recombinant protein 

• Recombinant cell-line 

Antibody Discovery

• Beacon® single B cell screening

• Display technology

• CAR-function based functional screening

• Hybridoma

• HCAb direct cloning screening 

𝙄𝙣 𝙑𝙞𝙫𝙤

• PK / PD 

• Efficacy 

• ADA 

• TOX 

CMC

• Stable cell line

• Process development

• Manufacture

IND

IDEA

Immunization

Protein 

Cell Line 

DNA 

mRNA 

𝙄𝙣 𝙑𝙞𝙩𝙧𝙤

Functional characterization 

Binding / Affinity 

Antibody production 

In vitro functional assay 

Developability 

Engineering

Affinity maturation 

Humanization 

Fc-Engineering 

Structure-Based Protein Design  

Antigen Design

Target assessment 

Recombinant protein 

Recombinant cell-line 

Antibody Discovery

Beacon® single B cell screening  

Display technology 

CAR-function based functional screening 

Hybridoma 

HCAb direct cloning screening 

𝙄𝙣 𝙑𝙞𝙫𝙤

PK / PD 

Efficacy 

ADA 

TOX 

CMC

Stable cell line  

Process development  

Manufacture  

IND

IDEA

Immunization

Protein 

Cell Line 

DNA 

mRNA 

Screening

Functional characterization 

Binding / Affinity 

Antibody production 

In vitro functional assay 

Developability 

Engineering

Affinity maturation 

Humanization 

Fc-Engineering 

Structure-Based Protein Design  

Antigen Design

Target assessment 

Recombinant protein 

Recombinant cell-line 

Discovery

Beacon® single B cell screening  

Display technology 

CAR-function based functional screening 

Hybridoma 

HCAb direct cloning screening 

In Vitro

PK / PD 

Efficacy 

ADA 

TOX 

CMC

Stable cell line  

Process development  

Manufacture  

IND

IDEA

+   Immunization
+   Screening
+   Engineering
+   Immunization
+   Screening
+   Engineering
+   Antigen Design
+   Discovery
+   In Vivo
+   CMC

IND

highlighted technology
Beacon® single B cell screening
CAR-function based antibody discovery
Bispecific/ multispecific antibody engineering
Protein engineering
CMC One-Stop Solution
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Nona’s Comprehensive Capabilities

Antigen design
Immunization
Discovery Platform
Antibody Screening
𝙄𝙣 𝙑𝙞𝙫𝙤 Pharmacology
Antibody Engineering
CMC

Antigen Design

Leveraging the expertise at Nona Biosciences, we collaborate closely with our clients to customize the preparation of target immunogens, ensuring the development of ideal antibodies that align perfectly with our clients’ requirements. Our dedicated scientific team is skilled in preparing a variety of different types of immunogens, including target-expressing cell lines, recombinant proteins, mRNA-LNPs, plasmids, and peptides.

Immunization

After decades of operation, Nona team has fine-tuned the immunization procedure to ensure the induction of a robust immune response against the target. Depending on the type of immunogen, the immunization route, frequency, and dose are carefully selected to increase the likelihood of obtaining a diverse range of antibodies.

Discovery Platform

Nona’s technology team has developed a range of powerful and innovative platforms to complement H2L2 and HCAb Harbour Mice®. These diverse platforms maximize the potential for screening lead antibodies that align with our clients’ specific requirements.

Antibody Screening

Nona’s experienced scientific team has developed a series of assays to comprehensively evaluate the antibody’s functions. Furthermore, we collaborate closely with our clients to establish target-specific assays, ensuring the finalization of the lead antibodies to meet their specific requirements.

In Vivo Pharmacology

In vivo studies serve as the bridge connecting basic research to clinical trials, representing one of the most critical aspects of therapeutic antibody development. The Nona team has meticulously established a range of animal models to validate the pharmacodynamics, therapeutic efficacies, immunogenicities, and toxicities of lead antibodies.

Antibody Engineering

Our accomplished scientific team has effectively implemented both phage display and yeast display technology platforms for protein engineering. These platforms play a pivotal role in substantially elevating the development potential of lead antibodies.

CMC

To expedite the drug development process, Nona is proud to offer an extensive array of CMC services, including the development of stable cell lines and scalable process development, manufacturing, comprehensive product testing, and the preparation of CMC submission dossiers.

Reach out to us for more information

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